{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY, date2num\n",
    "import numpy as np\n",
    "import os\n",
    "import warnings \n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'F:\\HQData\\market_A\\\\'\n",
    "\n",
    "zcfw = path + '002459.csv'\n",
    "index = path +'index_SZZS.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Open</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>Volume</th>\n",
       "      <th>curvalue</th>\n",
       "      <th>signType</th>\n",
       "      <th>dkWarnType</th>\n",
       "      <th>bonusInfo</th>\n",
       "      <th>bonusInfoType</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-21</th>\n",
       "      <td>82.20</td>\n",
       "      <td>81.75</td>\n",
       "      <td>77.79</td>\n",
       "      <td>79.05</td>\n",
       "      <td>80.10</td>\n",
       "      <td>15436298.0</td>\n",
       "      <td>1.220276e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-20</th>\n",
       "      <td>85.02</td>\n",
       "      <td>79.98</td>\n",
       "      <td>78.40</td>\n",
       "      <td>80.10</td>\n",
       "      <td>78.48</td>\n",
       "      <td>22007673.0</td>\n",
       "      <td>1.806183e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-19</th>\n",
       "      <td>80.02</td>\n",
       "      <td>79.30</td>\n",
       "      <td>77.44</td>\n",
       "      <td>78.48</td>\n",
       "      <td>78.51</td>\n",
       "      <td>12881479.0</td>\n",
       "      <td>1.011830e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-18</th>\n",
       "      <td>78.51</td>\n",
       "      <td>71.41</td>\n",
       "      <td>71.41</td>\n",
       "      <td>78.51</td>\n",
       "      <td>71.37</td>\n",
       "      <td>22443395.0</td>\n",
       "      <td>1.702299e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-15</th>\n",
       "      <td>72.99</td>\n",
       "      <td>69.34</td>\n",
       "      <td>68.30</td>\n",
       "      <td>71.37</td>\n",
       "      <td>69.35</td>\n",
       "      <td>13507974.0</td>\n",
       "      <td>9.563695e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             High   Open    Low  Close  preclose      Volume      curvalue  \\\n",
       "Date                                                                         \n",
       "2021-10-21  82.20  81.75  77.79  79.05     80.10  15436298.0  1.220276e+09   \n",
       "2021-10-20  85.02  79.98  78.40  80.10     78.48  22007673.0  1.806183e+09   \n",
       "2021-10-19  80.02  79.30  77.44  78.48     78.51  12881479.0  1.011830e+09   \n",
       "2021-10-18  78.51  71.41  71.41  78.51     71.37  22443395.0  1.702299e+09   \n",
       "2021-10-15  72.99  69.34  68.30  71.37     69.35  13507974.0  9.563695e+08   \n",
       "\n",
       "            signType  dkWarnType bonusInfo  bonusInfoType  \n",
       "Date                                                       \n",
       "2021-10-21         0           0       NaN              0  \n",
       "2021-10-20         0           0       NaN              0  \n",
       "2021-10-19         0           0       NaN              0  \n",
       "2021-10-18         0           0       NaN              0  \n",
       "2021-10-15         0           0       NaN              0  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#df = pd.read_csv(zcfw, index_col='times')\n",
    "file = index\n",
    "file = zcfw\n",
    "if not os.path.exists(file):\n",
    "    print('do not exist')\n",
    "else:\n",
    "    df = pd.read_csv(file)\n",
    "    df.rename( columns={'times':'Date','openp':'Open', 'highp':'High','lowp':'Low',\"nowv\":'Close','curvol':\"Volume\"}, inplace=True)\n",
    "    df.set_index('Date', inplace=True)\n",
    "    df.index = pd.to_datetime(df.index)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "def candleVolume(df, candletitle='a', bartitle=''):\n",
    "    from matplotlib import pyplot as plt\n",
    "    from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY, date2num\n",
    "    from mpl_finance import candlestick_ohlc\n",
    "    import numpy as np\n",
    "    import warnings \n",
    "\n",
    "    warnings.filterwarnings('ignore')    \n",
    "    plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "    plt.rcParams['axes.unicode_minus'] = False\n",
    "    plt.figure(figsize=(16, 8))\n",
    "    \n",
    "    seriesData = df.copy()\n",
    "    \n",
    "    Date = [date2num(date) for date in seriesData.index]\n",
    "    seriesData.index = list(range(len(Date)))\n",
    "    seriesData['Date'] = Date\n",
    "    \n",
    "    listData = zip(seriesData.Date, seriesData.Open, seriesData.High, seriesData.Low, seriesData.Close)\n",
    "    ax1 = plt.subplot(211)\n",
    "    ax2 = plt.subplot(212)\n",
    "    for ax in ax1, ax2:\n",
    "        mondays = WeekdayLocator(MONDAY)\n",
    "        weekFormatter = DateFormatter('%Y%m%d')\n",
    "        ax.xaxis.set_major_locator(mondays)\n",
    "        ax.xaxis.set_minor_locator(DayLocator())\n",
    "        ax.xaxis.set_major_formatter(weekFormatter)\n",
    "        ax.grid(True)\n",
    "        \n",
    "    ax1.set_ylim(seriesData.Low.min()-1, seriesData.High.max() + 1)\n",
    "    ax1.set_ylabel('蜡烛图及收盘价线')\n",
    "    candlestick_ohlc(ax1,listData, width=0.7, colorup='r', colordown='g')\n",
    "    plt.setp(plt.gca().get_xticklabels(), \\\n",
    "            rotation=20, horizontalalignment='center')\n",
    "    ax1.autoscale_view()\n",
    "    ax1.set_title(candletitle)\n",
    "    \n",
    "    \n",
    "    ax1.plot(seriesData.Date, seriesData.Close, color='black', label='收盘价')\n",
    "    ax1.legend(loc='best')\n",
    "    \n",
    "    ax2.set_ylabel('成交量')\n",
    "    ax2.set_ylim(0, seriesData.Volume.max()*2)\n",
    "    ax2.bar(np.array(Date)[np.array(seriesData.Close >= seriesData.Open)],\n",
    "           height = seriesData.loc[:,'Volume'][np.array(seriesData.Close >= seriesData.Open)],\n",
    "           color='r', align='center')\n",
    "    ax2.bar(np.array(Date)[np.array(seriesData.Close< seriesData.Open)],\n",
    "           height = seriesData.loc[:,'Volume'][np.array(seriesData.Close < seriesData.Open)],\n",
    "           color='g', align='center')\n",
    "    ax2.set_title(bartitle)\n",
    "    return(plt.show())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Open</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>Volume</th>\n",
       "      <th>curvalue</th>\n",
       "      <th>signType</th>\n",
       "      <th>dkWarnType</th>\n",
       "      <th>bonusInfo</th>\n",
       "      <th>bonusInfoType</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-21</th>\n",
       "      <td>82.20</td>\n",
       "      <td>81.75</td>\n",
       "      <td>77.79</td>\n",
       "      <td>79.05</td>\n",
       "      <td>80.10</td>\n",
       "      <td>15436298.0</td>\n",
       "      <td>1.220276e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-20</th>\n",
       "      <td>85.02</td>\n",
       "      <td>79.98</td>\n",
       "      <td>78.40</td>\n",
       "      <td>80.10</td>\n",
       "      <td>78.48</td>\n",
       "      <td>22007673.0</td>\n",
       "      <td>1.806183e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-19</th>\n",
       "      <td>80.02</td>\n",
       "      <td>79.30</td>\n",
       "      <td>77.44</td>\n",
       "      <td>78.48</td>\n",
       "      <td>78.51</td>\n",
       "      <td>12881479.0</td>\n",
       "      <td>1.011830e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-18</th>\n",
       "      <td>78.51</td>\n",
       "      <td>71.41</td>\n",
       "      <td>71.41</td>\n",
       "      <td>78.51</td>\n",
       "      <td>71.37</td>\n",
       "      <td>22443395.0</td>\n",
       "      <td>1.702299e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-15</th>\n",
       "      <td>72.99</td>\n",
       "      <td>69.34</td>\n",
       "      <td>68.30</td>\n",
       "      <td>71.37</td>\n",
       "      <td>69.35</td>\n",
       "      <td>13507974.0</td>\n",
       "      <td>9.563695e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             High   Open    Low  Close  preclose      Volume      curvalue  \\\n",
       "Date                                                                         \n",
       "2021-10-21  82.20  81.75  77.79  79.05     80.10  15436298.0  1.220276e+09   \n",
       "2021-10-20  85.02  79.98  78.40  80.10     78.48  22007673.0  1.806183e+09   \n",
       "2021-10-19  80.02  79.30  77.44  78.48     78.51  12881479.0  1.011830e+09   \n",
       "2021-10-18  78.51  71.41  71.41  78.51     71.37  22443395.0  1.702299e+09   \n",
       "2021-10-15  72.99  69.34  68.30  71.37     69.35  13507974.0  9.563695e+08   \n",
       "\n",
       "            signType  dkWarnType bonusInfo  bonusInfoType  \n",
       "Date                                                       \n",
       "2021-10-21         0           0       NaN              0  \n",
       "2021-10-20         0           0       NaN              0  \n",
       "2021-10-19         0           0       NaN              0  \n",
       "2021-10-18         0           0       NaN              0  \n",
       "2021-10-15         0           0       NaN              0  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from candle import candleVolume\n",
    "df1 = df[df.index> '2021-09-01']\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "candleVolume(df1, candletitle='蜡烛图', bartitle='成交量')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2629.000000\n",
       "mean       17.007231\n",
       "std        10.915661\n",
       "min         5.310000\n",
       "25%        11.090000\n",
       "50%        14.180000\n",
       "75%        18.140000\n",
       "max        80.100000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Close = df.Close\n",
    "\n",
    "Close.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_value = 5\n",
    "max_value = 85\n",
    "gap = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BreakClose</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-21</th>\n",
       "      <td>80.0</td>\n",
       "      <td>79.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-20</th>\n",
       "      <td>85.0</td>\n",
       "      <td>80.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BreakClose  Close\n",
       "Date                         \n",
       "2021-10-21        80.0  79.05\n",
       "2021-10-20        85.0  80.10"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BreakClose = np.ceil(Close/ gap) * gap # 调整数据到区间\n",
    "BreakClose.name = 'BreakClose'\n",
    "pd.DataFrame({'BreakClose': BreakClose, \\\n",
    "             'Close':Close}).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "volume = df.Volume\n",
    "PrcChange = Close.diff()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加个增大对应交易日期的成交量\n",
    "UpVol = volume.replace(volume[PrcChange > 0], 0)\n",
    "UpVol[0] = 0\n",
    "DownVol = volume.replace(volume[PrcChange <= 0], 0)\n",
    "DownVol[0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 价格变化区间中的成交量之和\n",
    "def Voblock(vol):\n",
    "    return ([np.sum(vol[BreakClose == x]) for x in range(5, 90, gap)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "cumUpVol = Voblock(UpVol)\n",
    "cumDownVol = Voblock(DownVol)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 0.00000000e+00],\n",
       "       [1.31573762e+09, 1.27132795e+09],\n",
       "       [2.03390774e+09, 2.21292662e+09],\n",
       "       [2.16579136e+09, 2.22882504e+09],\n",
       "       [5.67896572e+08, 5.57169596e+08],\n",
       "       [6.37544102e+08, 5.23633075e+08],\n",
       "       [4.64199975e+08, 6.70904843e+08],\n",
       "       [5.43971753e+08, 4.82739372e+08],\n",
       "       [3.24299961e+08, 2.42816396e+08],\n",
       "       [1.40345262e+08, 1.66431285e+08],\n",
       "       [1.41312954e+08, 6.88935430e+07],\n",
       "       [1.18973783e+08, 9.89560150e+07],\n",
       "       [1.69711360e+08, 1.34344653e+08],\n",
       "       [1.37552384e+08, 1.18500050e+08],\n",
       "       [7.55249580e+07, 3.46233820e+07],\n",
       "       [3.37376870e+07, 6.48274240e+07]])"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllVol = np.array([cumUpVol, cumDownVol]).transpose()\n",
    "AllVol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax1 = ax.twiny()\n",
    "ax.plot(Close)\n",
    "\n",
    "ax.set_title('不同价格区间的累计成交量图')\n",
    "ax.set_ylim(min_value, max_value)\n",
    "ax.set_xlabel('时间')\n",
    "\n",
    "plt.setp(ax.get_xticklabels(), rotation = 20, horizontalalignment='center')\n",
    "# 1.barh(bottom=range(min_value, max_value, gap), width=AllVol[:, 0], height=2, color = 'g', alpha = 0.2)\n",
    "# 1.barh(bottom=range(min_value, max_value, gap), width=AllVol[:, 1],  height=2, left=AllVol[:, 0], color = 'g', alpha = 0.2)\n",
    "ax1.barh(y=range(min_value, max_value, gap), width=AllVol[:, 0], height=2, color = 'r',alpha = 0.2)\n",
    "ax1.barh(y=range(min_value, max_value, gap), width=AllVol[:, 1],  height=2, left=AllVol[:, 0], color = 'r',alpha = 0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00000000e+00, 1.31573762e+09, 2.03390774e+09, 2.16579136e+09,\n",
       "       5.67896572e+08, 6.37544102e+08, 4.64199975e+08, 5.43971753e+08,\n",
       "       3.24299961e+08, 1.40345262e+08, 1.41312954e+08, 1.18973783e+08,\n",
       "       1.69711360e+08, 1.37552384e+08, 7.55249580e+07, 3.37376870e+07])"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllVol[:, 0]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "成交量+均线 制定交易策略\n",
    "\n",
    "1. 获取成交量 volume， 计算5日平均数  VolSMA5  VolSMA10\n",
    "    VOLSMA = (VOLSMA5 + VOLSMA10) / 2\n",
    "2. 获取价格\n",
    "3. 根据成交量制定成交量信号， 当 volume > VolSMA 释放买入信号， 小于 卖出\n",
    "4. 均线交易信号  5日上穿20日， 买入； 向下穿破20日， 卖出\n",
    "5. 合并信号： 都买入时， 进场\n",
    "6） 评价交易策略\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2021-10-08    18361147.45\n",
       "2021-09-30    19473576.05\n",
       "2021-09-29    20841871.70\n",
       "2021-09-28    20963790.30\n",
       "2021-09-27    22300497.05\n",
       "Name: Volume, dtype: float64"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volume = df.Volume\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_volume_signal(df):\n",
    "    volume = df.Volume\n",
    "    \n",
    "    volsma5 = volume.rolling(5).apply(np.mean)\n",
    "    volsma10 = volume.rolling(10).apply(np.mean)\n",
    "    volsma = ((volsma5 + volsma10)/2).dropna()\n",
    "    \n",
    "    volsignal = (volume[-len(volsma):] > volsma) * 1\n",
    "    volsignal[volsignal == 0] = -1\n",
    "    \n",
    "    return volsignal\n",
    "\n",
    "# 定义向上突破\n",
    "\n",
    "def upbreak(Line, RefLine):\n",
    "    signal = np.all([Line > RefLine, Line.shift(1) < RefLine.shift(1) ], axis=0)\n",
    "    return (pd.Series(signal[1:], index=Line.index[1:]))\n",
    "\n",
    "def downbreak(Line, RefLine):\n",
    "    signal = np.all([Line < RefLine, Line.shift(1) > RefLine.shift(1)], axis=0)\n",
    "    return (pd.Series(signal[1:], index=Line.index[1:]))\n",
    "\n",
    "\n",
    "def get_close_signal(df):\n",
    "    close = df.Close\n",
    "    \n",
    "    closesma5 = close.rolling(5).apply(np.mean)\n",
    "    closesma10 = close.rolling(10).apply(np.mean)\n",
    "    closesma = ((closesma5 + closesma10)/2).dropna()\n",
    "    \n",
    "    UpSMA = upbreak(closesma5[-len(closesma20):], closesma20) * 1\n",
    "    DownSMA = downbreak(closesma5[-len(closesma20):], closesma20) * 1\n",
    "\n",
    "    SMAsignal = UpSMA - DownSMA\n",
    "    return SMAsignal\n",
    "    \n",
    "def change_sigal_to_tradesignal(signal):\n",
    "    trade = signal.replace([2, -2,1,-1,0],[1,-1,0,0,0])\n",
    "    trade = trade.shift(1)[1:]\n",
    "    return trade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2620.000000\n",
       "mean       -0.093511\n",
       "std         1.047568\n",
       "min        -2.000000\n",
       "25%        -1.000000\n",
       "50%        -1.000000\n",
       "75%         1.000000\n",
       "max         2.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volsignal =volsignal[-len(SMAsignal):]\n",
    "\n",
    "signal = volsignal + SMAsignal\n",
    "signal.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2010-08-11    0.0\n",
       "2010-08-12    0.0\n",
       "2010-08-13    0.0\n",
       "2010-08-16    0.0\n",
       "2010-08-17    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade = change_sigal_to_tradesignal(signal)\n",
    "trade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6379310344827587"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 求胜率\n",
    "ret = ((close - close.shift(1))/ close.shift(1))\n",
    "ret.name = 'stockRet'\n",
    "tradeRet = trade * ret\n",
    "tradeRet.name = 'tradeRet'\n",
    "\n",
    "winRate = len(tradeRet[tradeRet > 0]) / len(tradeRet[tradeRet != 0])\n",
    "winRate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48633879781420764"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ret[ret > 0]) / len(ret[ret != 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18684e9a3d0>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(1+ret).cumprod().plot(label='stockRet')\n",
    "(1+tradeRet).cumprod().plot(label='tradeRet')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Hold(signal):\n",
    "    hold = np.zeros(len(signal))\n",
    "    for index in range(1, len(hold)):\n",
    "        if hold[index - 1] == 0 and signal[index] == 1: # 没持仓， 买入\n",
    "            hold[index] = 1\n",
    "        elif hold[index - 1] == 1 and signal[index] == 1: # 有持仓， 买入\n",
    "            hold[index] = 1\n",
    "        elif hold[index - 1] == 1 and signal[index] == 0: # 有持仓， 卖出\n",
    "            hold[index] = 1\n",
    "        \n",
    "    return (pd.Series(hold, index=signal.index))\n",
    "            \n",
    "hold = Hold(trade)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 交易模拟函数\n",
    "def TradeSim(price, hold):\n",
    "    price = price.sort_index()\n",
    "    position = pd.Series(np.zeros(len(price)), index=price.index)\n",
    "    position[hold.index] = hold.values\n",
    "    cash = 20000 * np.ones(len(price))\n",
    "    \n",
    "    for t in range(1,len(price)):\n",
    "        if position[t - 1] == 0 and position[t] == 1:\n",
    "            cash[t] = cash[t-1] - price[t] * 1000\n",
    "        \n",
    "        if position[t - 1] == 1 and position[t] == 0:\n",
    "            cash[t] = cash[t-1] + price[t] * 1000\n",
    "            \n",
    "        if position[t-1] == position[t]:\n",
    "            cash[t] = cash[t - 1]\n",
    "            \n",
    "    asset = cash + price * position * 1000\n",
    "    \n",
    "    asset.name = 'asset'\n",
    "    account = pd.DataFrame({'asset': asset, 'cash': cash, 'postion': position})\n",
    "    return (account)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asset</th>\n",
       "      <th>cash</th>\n",
       "      <th>postion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-15</th>\n",
       "      <td>22830.0</td>\n",
       "      <td>22830.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-18</th>\n",
       "      <td>22830.0</td>\n",
       "      <td>22830.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-19</th>\n",
       "      <td>22830.0</td>\n",
       "      <td>22830.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-20</th>\n",
       "      <td>22830.0</td>\n",
       "      <td>22830.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-21</th>\n",
       "      <td>22830.0</td>\n",
       "      <td>22830.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              asset     cash  postion\n",
       "Date                                 \n",
       "2021-10-15  22830.0  22830.0      0.0\n",
       "2021-10-18  22830.0  22830.0      0.0\n",
       "2021-10-19  22830.0  22830.0      0.0\n",
       "2021-10-20  22830.0  22830.0      0.0\n",
       "2021-10-21  22830.0  22830.0      0.0"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TradeAccount = TradeSim(close, hold=hold)\n",
    "TradeAccount.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000186851A1BE0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000186E0CAE3D0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000186E0CC15E0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7AAAAHzCAYAAAAZ7vEpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd5xU1cH/8c+Z2d5h2V06Sy9KXwuCCggKGk3s3fgzT4wxzUcTo4np1hSfRGOJNYlJTNTYoqIEFcGCCoo0QUBpS9mF7X135vz+uHeHrbA7O8vM7H7frxcvZs69c++ZsrvzvacZay0iIiIiIiIikc4T7gqIiIiIiIiIdIQCrIiIiIiIiEQFBVgRERERERGJCgqwIiIiIiIiEhUUYEVERERERCQqKMCKiEi3M8ZkGGMSjTGx4a5LZxljUo0xJtz1aGSMiYmk+nSWMSY+3HUQEZHopQArIiJHwveBHwDXGWP+aIxp8++PMWapMebMUJ7YGHOCMeaWTuz/L2PMhU2Kbgd+eIj9FxhjJjS5f54x5uRDPMfHjTFnNLlvjDGLjTGTOljFBcC/O7Jjd7ye7ZxjbAf3/TLwz+6sj4iI9Gwx4a6AiIj0CtWABX4H/BWYBSxrY7+PgPnAf9o7kDFmCZDqHrOpRKDUWntqi/JxwIB2juUBYq21tU2Ka4C6JvfnAGfQBvfxvwMub1I8E9gGzDPGlFlrf9Nk//8HXARMMsb8FNgDvOw+5nFjTB0QjxOaNwEPNDlutbV2PnAuMM0Y87Zbfoe19uW26kfHXs9ngIlAeYtN8UC5tfaEJvtWu/UCGGetTcB5vWrd7VfhvJ5/aud0rwE3GmOSrLVV7dVJRESkPQqwIiISUsaYU4Df44YaVzbgBc4BYnHC2hjgC6AQJ9wCGMAaY1a69z3uY+dYaze7ZbU4AbYt9W2UDQLOMMYc18Y2j1uHc5uU2cb6uC2LE4D3W/TaHWOtLQMuA54Faowxp1hrXwcqcULdncC5xphjrbUfGGPmAzcAQ3AC6j9xAuZbwHCc1+zvjWHUGDMVJwhvA94G7jTG5AAXAF8BlgBrgBXu/l6Cez1r2nhdGtW1uL8LOBtIoUUrsDFmJnAzMK9F+T1Ay4sKHzV5PT+11p59iDqIiIgEKMCKiEioJQOrrLVXNhYYY84C5ltrv2OM8Vprfe6moe54zgeBN6y1/3L3vwA4C7gGp6XV3+T4K3GCasvglYATjlsaDlxnrX3+UJV2A+NTOAFvvjHmUmA7cLO19q4m++0H6owxo4FvAqcDjwBrjDGZwAk4ofkCoAhIAj7ACZzfwWkNfg0nXH4HeMI95z+BCmPMNGAdrVuYG3Bae58HrgUygJXW2gMA7msazOvpBe4GVrU432Dg+hZlfuBkYGCLY8wEfgScYa3d3uIxOcA11tqlLcoxxswGft2yXEREpD0aAysiIqFm2yjbjRPSwGl9PRPAGJMC/A2YhtMS2eg1wIcTqiZba60x5ivGmOXAdGAKcHyLf1OAKcaY5caY6U2ONRqnFfNwPgHGA88B38ZpLb0Q2GSM+VmT/WJwWoEX4ITR5Thh8in3eWwC/mmtPcV9/CpjTKa11rp1n4fTCjsRyHP/zQNGAFNxuvy2N9HR34GrcML6XcBPmm7szOvZZNujON2k78d9Dd1//YDftjh/4+P87vkaL4RXA18GthhjYluM/21o57k08h9mu4iISIBaYEVEJNQM8BVjzEac1th3geuAUcaYVJyWwF+4+z6EM3b1VpxWxZ+65ZfjhMT7cELlR24L6vPGmEeAAzitsDnu/vtwWl/7Wmu/3KI+5cAj7UzcOxD4k7X2F9ZaP+B397NAAU6X50+AHxhjnrXWrgWsG6jvt9bea4z5F/AzYAfwEnApcI7bpTbFLb/FrfNfcAJyo0E4AW9wk7JLrLXlbj0Wuq/DBUCytXaR20V7mLvvxcaYh621JZ19PZucLx1njO824MqWL5AxZpy1trGV1IPz3cGP8z4/j9PiPBTIBba42y/AaUWGtlvFm53iMNtFREQCFGBFRCTUknBaIK8xxpwNnGKt3WOM6Y/TqvmktXaru+9VOOMsM4GH3XBajdN19UvW2g1tHH8aBydaGuSW5QNx7r9mrLWnt1dRY8z9wOa2tllra4AP3f3OAm41xvwWpyUTnLGlN7nnrgWOwpmgaQNwHE5Q/xpOd+rPmhw6xVo7xe3q+7b7Wt3rnue1FtVYxMExsLcaY/6N81p9BScQ34rT6jnXWruGTrye7sWEJ3AC70hgLnAmzrjej9y6H4MTTAMPc49dAnistV8yxryK0zX5IeAia21Ri+eQCTxhjKls42WOwWmdFxER6RAFWBERCbUBQJl7OxXY797+AGfMaGC5GDckAhS63XT/ghOSbmsnvILzt+t59xyz3bKlQBpOgATAGDMEJ4CW0bqbagJwE05L5t/aOMcUt9vvo8aYGcC9wD9wWlMbJ4rKAb6H02X4V8AfrbXfds99rftcvwZc3OS4TVsjf4bTbfinxpjXga/ihODCdp43OEHxA7cu4LR8XuGG1069nm4r7x04Y4q/hROKz8Jpud1krf3IGHMuTkBvWv8cYC3Qcj3ad3FajP/eWOBOLDUVOMlauw4REZEuUoAVEZFQywOWGWPSccZSbjXGpOFMprTBWrvPGDMH+Mxa2zQcvQ/8Bqd17/W2DuwGphs4OEa02P1/l/v/TY37Wmt3Av0PVVFjzC9wx8e6QfUmnHGqr7vP4RLgl8CF1tpVxphE3MmVrLV7gAHGmGT3eY4BVrjjP7cD/4vTWtw0kO4DFrotuaOBP+B0s34Sp/vxRGtt4+RNCe5xk3EuCmRZawuNMbXW2jy3zg8C7S1Hc9jXE+d1/AD4L86Y3tPcejcunTPU3d7odJxQ/BIw1u1K3bjtGeAxY8w/m0zSNRsn8Ld3MUJERKRTFGBFRCRk3IA3H2fs5T9xuo8uAt4E/oQzNvZ6nC62rxtj/oMTli7Cadk7GWfZmmXGmA+BV3GWilntrtU6AqfbbGPIa+xCPN79P9YY8761tvQQdUwCTsEJhf1w1mIFp6XxaeCCxnVh3X1fsdaWGGNG4rSQFrnb5uGEub04XXxfN8ZMBO4BPsMZ13o8MNUYs9Na+5j7XO/GGVd7Hs7Y4B3ASThhdqMxZhFO8KwC7rPWPuCe70tNnsNq9+Zg93VuLB/QydfzfZxWVx/OpFAn4ITYWmPMOPf+75q8fBPc1+sM4BvGmItxZjG+GvjUfV6PGmOuxmn5vQv4nTu+WEREpMtM84kIRUREgmeMuQI4u3FdT2PMyTgh77vW2heNMUNxWvtGAifiTGj0X5wQ9krj7LhuED4PZxKlwTjLtGTgrD1axsHZcJuOgQUnNKUB51prCw5RzydwxtH+zVr7Vnv7tXjMBzhdaO+w1j7lzsCbba3d7W7/Mk54vc5a+5wxJhcnMA7E6UZbjNPS+oi19mn3MbcAxdba+9z7k4FLgB9ba9ucvdcY84W1drh7+0HgWWvtYneM8Wo6+Hpaa+vcbZfjLOfzCPCotdbndh3+PfBCk27Rs3FmLJ4L7MQZ83oH8B+crto3AKU4Af2PON3HfwKc33guERGRrlKAFRGRkDLGxDUJRx4g01pb2GR7LJBjrd1ljDG2B/0havrcG+8Dadba/Yd4WGfPkWStbbPbcHe+nu6kTwNaTEiFMaZfKJ+fiIjIoSjAioiIiIiISFTwHH4XERERERERkfBTgBUREREREZGooAArIiIiIiIiUUEBVkRERERERKKCAqyIiIiIiIhEBQVYERERERERiQoKsCIiIiIiIhIVFGBFREREREQkKijAioiIiIiISFRQgBUREREREZGooAArIiIiIiIiUUEBVkRERERERKKCAqyIiIiIiIhEBQVYERERERERiQoKsCIiIiIiIhIVFGBFREREREQkKijAioiIiIiISFRQgBUREREREZGooAArIiIiIiIiUUEBVkRERERERKKCAqyIiIiIiIhEBQVYERERERERiQoKsCIiIiIiIhIVFGBFREREREQkKijAioiIiIiISFRQgBUREREREZGooAArIiIiIiIiUUEBVkRERERERKKCAqyIiIiIiIhEBQVYERERERERiQoKsCIiIiIiIhIVFGBFREREREQkKijAioiIiIiISFRQgBUREREREZGooAArIiIiIiIiUUEBVkRERERERKKCAqyIiIiIiIhEBQVYERERERERiQoKsCIiIiIiIhIVFGBFREREREQkKijAioiIiIiISFRQgBUREREREZGooAArIiIiIiIiUUEBVkRERERERKJCTLgr0Fn9+vWzubm54a6GiIiIiIiIdINVq1btt9ZmtbUt6gJsbm4uK1euDHc1REREREREpBsYY7a3t01diEVERERERCQqBB1gjTF9jTHzjTH9QlkhERERERERkbYEFWCNMX2Al4BjgTeNMVnGmEeNMe8ZY25psl/QZSIiIiIiIsFau6sUv9+GuxoSYsGOgZ0EXG+tXeGG2bmA11o7wxjzmDFmNDAx2DJr7eZQPDkREREREel9Xl23h2v+9hG/O38y504fHO7qdFh9fT27du2ipqYm3FU5IhISEhg8eDCxsbEdfkxQAdZa+xaAMeYknFbYvsBT7ubFwCxgahfKFGBFRERERCQoKz4vAqCosi7MNemcXbt2kZqaSm5uLsaYcFenW1lrOXDgALt27WL48OEdflxXxsAa4EKgGLBAvrupCMgBkrtQ1vJcVxtjVhpjVhYWFgZbZRERERER6QVqG/wAJMR5w1yTzqmpqSEzM7PHh1cAYwyZmZmdbm0OOsBax7eANcAJQKK7KcU9bkUXylqe6yFrbZ61Ni8rq83lgERERERERACocwNsjCf6gmBvCK+NgnmuwU7i9ENjzBXu3QzgTpyuvwCTgW3Aqi6UiYiIiIiIdFhpdT3WOpM21Tb4AGjw+cNZJekGwU7i9BDwlDHmf4B1wPPAMmPMQGAhcDxOt+LlQZaJiIiIiIh0yM9fXM+f393GnedM5KJjhwZaYH/ywnoun5Eb3sr1YqtXrwZgypQpITtmUC2w1tpia+18a+1J1tprrbWlwGxgBTDHWltqrS0LtqyrT0pERERERHqPP7+7DYCX1uyhtLqexRv2hbdCAjgBtjHEhkqwLbCtWGuLOTibcJfLREREREREOqOwvJZX1+0JdzVC4hf/Wc+G3WUhPeaEgWn87Myj2t1eUVHBeeedR2VlJaNGjeL+++/n/PPPp6ysjMzMTJ5++mnq6+tbldXV1XHFFVdQUFDAxIkTue+++7j55pt57rnnAHjiiSd4/fXXQ/IcQhZgRUREREREwmlkdjJVdb5wVyNq7dmzh+985zvMmzePBQsWsGbNGjweD8uWLePFF1+koqKCrVu3tir785//zNFHH83Pf/5zzjnnHNasWcMdd9zB2LFjAbjyyitDVkcFWBERERERiVqVtQ2B26+s3Ut5zcH7Rw9KC0eVQuJQLaXdJTY2lkceeYTHH3+coqIisrOzOfroozn11FMZPXo0CxYsYNq0aa3KNm3axLvvvsvSpUspKSkhPz+fSZMmdUsdg15GR0REREREJNy2H6hqdn/55v2B295etCRNKDz66KOcd955PPnkkyQnJ1NaWsrMmTNZvHgxxcXFLF++nE8++aRV2dixY7nuuutYunQpt956K0OHDgUgMTGRqirn/WmcIbqrFGBFRERERCRqbdrnjBOdOCi91bZ6X2hCU28xf/587rjjDubOnQtAZWUl99xzDyeccAJ79+4lLy+P3NzcVmVf//rXWbRoESeddBIPPvggQ4YMCRzv2WefZebMmSxfvjwkdVQXYhERERERiVob95YT6zVMG5rB2vzmC5r4/N0bYNfuKiXGaxg/IHq7Kjd10kknsW7dumZlr732Wqv92ip76qnWc/L27duXJUuWhK6CqAVWRERERESi2Gd7yxmZlUJGUlygrG9yHGdMGkCD39/u4554bxv5JdVdOvfZ97/Dwj8sp8HX/nkktNQCKyIiIiIiUWvT3nKOGd6XpDhvoCwlPoYYj6GhnRbY4so6fvLCem5/ZSMrbj4l6HM3Hv/ORRv58Rnj+dVLn7LtQCWJcV5+9eWj6Zscd5gjtGatxfSSsbvBjItVgBURERERkahUWl3P7tIaxvZPJbFJgE2Oj8HrMTS0Mwa2uKoOgOp6H5N/ubjL9Xjk7S+YMjSDx975gn4p8eyvqOXMSQNZcHT/Th0nISGBAwcOkJmZ2eNDrLWWAwcOkJCQ0KnHKcCKiIiIiEhU2ryvHIBx/VPZX14XKE+J9xLr8bQ7Bra4qh6AueOymTWqX9Dn9xjIL6nm4eVf8O1/fAzA7WcfzdVPrGJ/RW2njzd48GB27dpFYWFh0HWKJgkJCQwePLhTj1GAFRERERGRqLRxrxNgx+SkYm15oDwtIZYYr6G+nbGppdVO2P3uKaOZMiSjy/Ww1mmFPXVCDnPHZeMxUFBW0+njxMbGMnz48C7XpydTgBURERERkai0aW85qfExDMpIZFBGIlmp8RSW1zIiKxmPMVTUNrT5uOJKpwW2T1JsSOqRk+Z0g81KjSfG6yE7NYHdpZ0PsHJ4moVYRERERESi0qZ95Yzpn4oxBmNMoDvwyKwUUuJjqG3wU9fQuhW2pNoJsBmJnZ9kqS2xXme8amOH5YEZCezu4gzH0jYFWBERERERiUpbCioYk5MSuH/hMUPwegynjM8hNcHpbNpWK2xJVR0eQ2CfrhrWLxmAsTmpAAzISGSPWmC7hboQi4iIiIhI1PH5LcVVdWSlHpzF9vgRmWy9/XQAUhKc7sEVNQ2tlrMpqaonPTEWjyc0M/3OGZvNM9fMYNrQPgAMykjkvxv28dTKnSE5fqidNqE/6SHqPn2kKcCKiIiIiEjUKa2ux9r2x7E2tq6W1dS32lZS7QTYUMrL7Ru4Pa5/KnUNfm58Zk1IzxEqU/43QwFWRERERETkSGlcy7VPUtvjWFPj2+9CXFXbQHJ890Whc6YN5oSR/Wjwtz0Lcrhlp3Zu7dVIogArIiIiIiJRp8QNsBnttsAe7EIMcKCiltLqekZkpVBZ10ByXPdGof7p0RsSI5kmcRIRERERkahzcCmctltgU9wuxO9uPQDAzLveYO7v3gKgqs5HUrz3CNRSQk0BVkREREREos7huhCnuF2EH3vnC+p9fmrqne681lpKquq7vQVWuofeNRERERERiTolVe5arsmHnsQJDnYjBhh+8ysAJMSqLS8aBfWuGWPSjTGLjDGLjTHPGWPijDGPGmPeM8bc0mS/oMt6qydWbGfar/6L328Pv7OIiIiISC9VXFVHjMcEJmtqKT7mYNRpayKn/OLqbqubdJ9gLztcCtxtrT0V2AtcBHittTOAEcaY0caYc4It6/rTCp8Nu8tYv7uUAxW1QT3+J8+vo6iyjiK3S4SIiIiIiLRWXFVPRlIsxrS9lqsxhjljs5g4KD0QYI8amBbY3p2zEEv3Cepds9be3+RuFnAZ8Hv3/mJgFjAVeCrIss3B1CsSfOX+d6hr8DMoI5F3bpp72P33ltawfHMh500fzPrdZc3K+6XEd2dVRURERESiVklVHRntjH9tFBfjoa7Bz55Sp7X1S5MGBr5ztzd2ViJbly47GGNmAH2AbUC+W1wETAOSu1DW8jxXA1cDDB06tCtV7nb3XTKN/27Yy1Mrd1HagQWS73ljM/94fwejslM4+/53A+XLN+8nJy2BrFSFWBERERGRpj7eUcyidXsZlJF4yP3iYrzU+fyszy/DGLjkuKEM7ZvE7pJqFk7sf4RqK6EU9MhlY0xf4F7gKqACaPz0pLjH7UpZM9bah6y1edbavKysrGCrfETMn5DD3HE5AOw4UHXY/bcfqARoFl4B7np1I9/+x0ehr6CIiIiISJT790e7AMgvOfQ41jiv0wK7bncpwzOTSU+M5YxJA/j6SSMY3CfpSFRVQiyoFlhjTBzwNHCztXa7MWYVTtffFcBkYBOwqwtlUW1EVjIAZ/7xbUZnpzTblpIQwwkjM9lbWstvzptEnLf1NYQ/XDSFZ1btYnsHArCIiIiISG+Tk5rQof2S4rxU1DaweV8F4wakdnOt5EgItgvx13C6+v7YGPNj4HHgcmPMQGAhcDxggeVBlkW14f2SA7dz0hJIS3Re5uo6H29uKuTjHSUAXDdvNHvLDk72lJuZxAvfnkV6Yiwf7yhh9c6Sbqujz2958oMdLDy6P1V1PnLSEoiL0VTiIiIiIhL5stOcYXZ3nTvxkPsNzEiktLqe0up65k/IORJVk24W7CRODwAPNC0zxrwIzAd+ba0tdctmB1sWzWK9Hm5aOI57Xt/MfZdMIz3JGQdrrWXWXW8Gujos/MNyKusOTun9j68fHxgzO7hPIuU1DewsqmJI39B3b/jt4k08sHQrK7cV8fzq3ZwwMpN/fD3qrx2IiIiISC+weqcTGRqH7rVnzrgs7np1IwB9kjVpU08QsiY3a22xtfYpa+3eUJRFu2tOHsm6n58WCK/gTOW97MY5/PGSqYCzHtXjVx5DfIyHEVnJDGwyCL3xCtFr60P/kpRW1/PA0q0APL96NwDvbj3A7sOMIRARERERiQRPfrADoM3heE2N65/GzFGZACTHebu9XtL9tPhRN/J4Wq9J5fUYiiqdNV7Pmz6Y2WOzWXnLvFbdd4dlJjM2J5XF6/cxrn8as0b3C6oOfr/l8/2V+PwWgPKaes578L02991+oKpZiBYRERERiTSN32sBYmPaXgO2qYxEp+U1MU7RpyfQuxgGp4zP4b43t3DNySMBSE1oe6mdYZlJLN6wj8sefZ9XrzuRpNgYhmZ2rjvxkx/u4MfPrevQvvsrag+/k4iIiIhIGD3+zheB24drgQWIdxuKDh91JRoowIbBoIxE3v/RvMPul9Skm8OC3y8H4NXrTmRc/7QOn2trQSWJsV5+d8HkQFlyfAxjc1JZvrmQHzyzhtHZKWwuqFCAFREREZGI97vFnwVue9vo8djSWVMG8tLaPUwektGd1ZIjRAE2gvls67INu8s6FGD3ldXw/Mf5LN1UwLDMJE6fOKDVPufnDeH8vCH4/ZbRtyyisLyWJRv2sWxzIb/88tGheAoiIiIiIiE1dWgG7249wJcmDcCYwwfY2WOz+ezWhUegZnIkKMBGsMpaZ4biP1w0hdljspn8y8Vs2lfeocf+z19WsjbfmZ3tycPMLuzxGHx+y/3uxE4APz5jPPExGuguIiIiIpGlpt7HzFGZ/PGSaeGuioSBAmwEa+wR4fUY0pNi6ZMUy5/e+pz84tazBSfEerl54TgyU+Kx1gbC6zdnj2TGyMxOn7ugrLZblu8REREREemK2gY/fZK0JE5vFbJldCT0rp0ziqQ4L8fm9gUgxh2k/smuEjbsKQv8W5dfyjOrdvHGxgL8fsuidc7SO/ExHm48bWyHzvXYlXn84LSxpMY71zSWbirohmckIiIiItI1NfU+EmLVU7C3UgtsBJs2tA8bfrkgcP/Jrx/H2vxSzp46uNl+Pr9l/E9fZXNBBX9/fzs/eWE9AA9ePr1D4wLAWQR67rgczpo8kBN//Sa/XfwZl8/IDdlzEREREREJha2FlYzKTgl3NSRM1AIbRUZlp7YKr+B0MR6ZlcLKbUV8uvfgGNlpQ/p0+hxD+iYxeUgG5TX1/G3F9mbrbImIiIiIhNPuEmco3c6i1kPqpHdQgO0hPAY+2lHCcx/lB8rSEoNrYL929kj8Fm55fh3//mhXqKooIiIiItIl+W6A/cbJI8JcEwkXBdgeIj0xFoDqel+grKPdh1s6fsTBSZ/+88nurlVMRERERKSLXlidzwl3vB5oge3IspLSMynA9hB3XzCl2f33f3RK0MdKT4zl2tkj8RiorvMd/gEiIiIiIt3olufWsbu0hg27ywDon54Q5hpJuCjA9hD90xOYMzYrcD8nrWs/1DcuGMfssdnUNvi7WjURERERkS7JSHZ6G368s4TEWC9pCZqLtrdSgO1BGmdj++qMYSE5XkKsh5p6tcCKiIiISHg1rvv6yc4S+qcnBD1UTqKfAmwPktsvGYDS6vqQHC8+xqsWWBEREREJuww3wNY2+Elz536R3kkBtgcZm5MKNJ+EqSviYzzUNqgFVkRERES6V1VdA8WVde1uT4n3Bm4nxCjC9GbqPN6D5OX25c3vz2a42xLbVQmxXmrqnRbY4so6vF5DWoKueImIiIhIaJ1579tsLaxk251ntLm98TspQHyst819pHdQgO1hQhVeoXkL7NRf/Zc4r4fPblsYsuOLiIiIiABsLawEoKymntT4mMAY1+LKOu55YzNFTVpnS6rab6mVnk8BVtrlBFg/1loA6nwaDysiIiIi3WfSzxczdWgG/77mBDwew69e2sCzH+c322fb/sow1U4igTqQS7viY71YC39/f0e4qyIi0mE7i6r474Z94a6GiIh00NV/Xdns/sc7SthfWUuDz8/iJr/P+6XEA1BVpzlaerOgA6wxJscYs7zJ/UeNMe8ZY24JRZmEX7w7QP6W59cFyt7beiBc1RER6ZBT/28ZX2/xZUhERCJTWU19s5AaKK+u55cvbaCitiFQtr+iFoDz8wYfsfpJ5AkqwBpj+gB/AZLd++cAXmvtDGCEMWZ0V8pC8cSk69oaIH/xwyu469WNYaiNiEjHVLvrVzcOfxARkcj19xVt9/TbX1HHq+v2ApDofic9fkRfln5/NjefPv6I1U8iT7AtsD7gQqDMvT8beMq9vRiY1cUyiQDJcQcD7A9OG8u1s0cC8Ke3toarSiIiHdbgV4AVEYl0ZTX1gdvfOHkEA9ITAKcbcZL7XTQvtw8rbj6Fh6/II7dfslbF6OWCCrDW2jJrbWmTomSgcXR1EZDTxbJmjDFXG2NWGmNWFhYWBlNlCcLssdnMHJXJK989kW/NGcWNC8bxrTlOiK1s0p1DRCScquoaeGLFdnwtAmtdgyaeExGJdOVugI2L8XDzwvG8d/MpJMd5KSyvJTHOmW+2qs5H//QEUhVchdBN4lQBJLq3U9zjdqWsGWvtQ9baPGttXlZWVoiqLIfTNzmOv//P8UwYmBYoy8vti9/CuvzSQzxSRPbUjEIAACAASURBVKT7WWspKK/hgaVb+cnz63hpze5m2xVgRUQiX0mVE2C/efLIQFlGUhyPvfMFn+0rB6BakzZJE6EKsKs42PV3MrCti2USofomxQFQWacWWBEJrzc3FXDsba/z2npnjNS2/VXNtmvpLxGRyLevrIbjhvflf+ePCZTll1QDBHrWZCSp5VUOCtU6sM8Dy40xA4GFwPGA7UKZRCivx1lUWt8LRSTc8oudLzif7atw/y9vtr22Xr+oREQiXX5xNcePzDzkPn+4aOoRqo1Egy61wFprZ7v/l+FMxrQCmGOtLe1KWVfqJN3LYxoDrCZHEZHw8nqa/wn7vMXC9nU+dTkTEYlk9T4/e8tqGJSR2Kz8hW/N5KQxWZw1eSDv3DSXrNT4MNVQIlGoWmCx1hZzcDbhLpdJZGpsgfVreQoRCbPGyeTevWku1/1zNR9sK6KhSfeQWo2BFRGJaNsPVOK3tAqwk4dk8Nerjg1TrSTShWoMrPQSXvcToxZYEQmn5ZsLue2VTwHISUtgUB/ny8/qnSWBfTSJk4hIZHt5jTOHwbRhfcJcE4kmIWuBld6hsQvxoVpgG3x+/rTsc97YWMDwfsmYIM7j9Rj+58ThjMpObbWtvKaem55dy4mj+nHGpAGaUl2kF7r80Q8AyEqNx+sxfHvuKJ77OJ/zHnwvsI9aYEVEItt/P93LtKEZjMlp/X1PpD0KsNIphxsDu7OoipufXcvbW/YDsMedRa6zdpfW0Dc5jhsXjAuUVdU18Nm+Cr5y3zsAvLxmDz9+fh3Lb5zDwBZdT0Skd2hc8D41ofWfM7XAiohEps37ypn/f8sAuGnhuMPsLdKcAqx0ysFZiFsH2Oc+3sX//uuTwP0tty0kxhtcL/Xjbl/C/opaAPx+y69e3sCq7cWs2eXM8ZWVGk9heS0+v+XJD3Zww6ljeXjZ5xw1KI0v9lfi81uumJEb1LlFJHo0BtiEWG+rbQqwIiKR6XZ3CAjAqRNywlgTiUYKsNIpnkNM4vTk+zsDt78zd1TQ4RWgX0o8+yvqAPh0bxmPv7MtsC09MZb3bprLjqIq5v7uLe59YwtvbipgXX5Zs2MowIr0TLuKD673OjIrBYD4mNa/b9SFWEQk8hSW1wZ66gGMcH+Pi3SUAqx0ite0vw7stgMHl7D41pxRXTpPvxSnhdVayxn3vB0oT02IYdH3TiTG62FYZnKgvGV4BbDWYkwwI3BFJJK9snYPAKdP7M+35zq/a+K8HoyBptfWymvqw1E9ERE5hDc3FVDvs7z0nVkcNTAt3NWRKKRZiKVTGpdd9LVogS2rqaegvJZrTh7JtjvPaLM7X2dkpcZTUF7Dgcq6ZuVv3zg3MN7V6zH88stHcUxuH86YNID+aQlcN280V80cDqj1RaSnWr+7jEEZidx/6XSS4pzrsMYYYt1fUBfmDQGgoLyWFZ8faLa0joiIhFdFjbME2pA+SWpokKCoBVY6pbEF1t9iDOylD78f0vMMykikoLyWa//2UaDs6pNGkJ7UfMbhK2bktuoq/Nf3tgHOGpFdDdIiEjkOVNRSVtPAC6t3M2dsVqvtx43oy+qdJcybkMPiDXt57uN87v7vZ/xwwTi+OXtkGGosIiItVdU5ATYxTt/RJDgKsNIp3jbGwG7YXcbafGdypUuPGxqS88wdl83STQXU+vycNCaL354/iezUhA49trFFprLWR6aGVYj0GGf98R3y3ZnNpwxpvWbgE187LnA7Jy2BjXvLAdhXVnNkKigiIodVWecjzushro25C0Q6QgFWOsXTxizE69zw+uK3ZzKkb1JIzjN5SAYvfHtWUI9Ndq/oVbpX+ER6opp6H+U1DWSlxoe7KkdEvc8fCK8AFx075JD7ZzcJsP1S4rq1biIi0nHlNfUkxav1VYKnSx/SKY3rwDZtgb3x32sAyEyJjC/SyfHOdZkqBVjpwb72lw855rYlvLtlP7tLqiksd5adqq7zYduYJTzaNV4oa5STdugeGdlNgn18jL4oiYh0h1Xbi9lZVHX4HZvYW1pL/8P8Dhc5FLXASqccahbiPi3Gp4ZLsntVr6LWF+aaiHSP/JJq3tlyAIBLHnmfOK+HOp+feeNzWPLpPi7MG8INp43he0+ubtZqGW1GZ6dw2YxhzBmbTVGTCd3SEg7/p6tv8sFW15p6/S4QEekO5z7wLgBbblvYoeUTfX7L2vwSpg1tPQxEpKMUYKVTArMQ+w8m2OnD+rC3tCYw9jTcGltg39myn5PHtJ7oRSTa/fAZp9fDwPQEdpfWUOdeUVry6T4A/rVyJ/9a6azLfExuHwb3CU3X/iNp/e5SXt9YwOsbC/jZmRP4ZGcJAPdePJXjhvc97OObTuBWXe/jox3FDM9Mpk+yuhOLiITaWX98h39/84RmZR7PwR4wO4uqOPHXbwa2nTl54BGtn/QskZE4JGrEeT1kpcazfvfBdVdr6n2M658axlo1l+NO9vT4O1/wo9PHh7k2IqFVXefj7S378Rj497UnMOOON5gyJIMfnT6ezwsrqKrz8ff3t7O10FmX+QenjePYDgS+SLN6Zwlfue8dAH7xnw2B8ilDMsjuQNezxCYB9oXVu7l/6Va+NGkAf7xkWugrKyLSCzUdqrVhTxnjf/pqs+0eA3+56lhOHJ3FRQ+taLbtlPHZR6SO0jMpwEqnGGOYOiSDzQUVgbLK2gaS4iPno9QnOY5vzh7JA0u3Utfg1yx30qOsdlsi775gCgPSE3n/R6fQJymOuBhPIKheNWs41lo+3VPO+AGRc3GpM0Zntz2FeEeXXUiMPfhz39iN+v0virpeMRERAeCTnaWtym5aOA4Aa+GuVzdy+aMfMCA9gT2lzmzwvzjrKM6dPlhzE0iXRE7qkKiRnRbPh9uK2FVcRVl1AxW1PlIibDa5HHcCl4raBvrGdF+XwdU7Sxg/IJWNe8rplxrPoIzEbjuX9G6f7ikjLsbD9/75MYMyEpkzzrl63d5kRsYYJgxMO5JVDKnkdi6KJXdwqEJbQTe1A2NnRUTk0Px+y1/f28aqHc4F1aXfn82tL29gzrhsLj1uWGC/h5d/TlFlXSC8/uj0cXz1hNww1Fh6Gv01l05LTYiluKqeWXcdHMvQ0S+VR0pqgjOhVGl1fbPJXDpq/e5SNrlLcLSnpKqeX760oVnZ2z+cE5XjDeXIK62u5/6lW9i23+nqazAYg/MPA858aRSW17JhdxkVtQe7at117kTSEyNj0rTu9PoNJ/PJzhKuf+oTfn3eJMbkpHa8BbbF76QJA9IoqaprZ+/ea8eBKvokxwZ+Z4pI73XHok/5bG85HmMwxuAxzuoTHo9zUTRvWB/OzxvC0T97rdnjBmQk8MhXj2l1vP/+70lMv3UJAN+cPZLzpx96+TORjoqs1CFRoa1WjPZaS8JlpNv9cMPuMob3Sz7s/tZadhVX88raPZw9dRBX/3VVULO3funet/nBaWN5cfVuLHDO1EFcdOzQTh9HejZrLRc/tIKNe8sYne108bVYrAXbZB8LfO6OZY3xGLJS47lq5nC+PGVQeCp+hI3MSmFkVgrH5Pbt9BrT8S2GDhyT24cnVmynwefv0EyZvYG1lpN+8yaTB6cHve62iPQM1XU+/vTW5wxIT6Bvchx+6/4dss7SiZsLKnh5zZ5mcxI0aq87cGZKPH+4aAr1Pst50wd391OQXiSyUodEhWF9DwbCIX0T2VlUzeA+kdV19qiBaSTFebn3jc2cMj672YykLVlr+erjH7Lss0IA7li0EXCuFl50zKGvFibFxZCWGEODz7J6ZwmXPvI+P35uHcMyk6it9/P7JZu5IG8IHo8J3ZOTqGat5ZlVu9iwp4ybFo7jmpNHHnL/AxW1vPjJbi45bmivHTPU2fAKgQbsgFE5qfgtFFbUMiA9fL+vPttXzrMf5fPDBWMxJry/F8pqnFb9T3aVMuGnr/LAZdM1c7tIL/X5fmduk5sWjmvzIunbm/dz2aPvB+6fMXEAH2wrOuzvjN5ywVWOLAVY6bSFR/fnrMkDOWpgGnm5fflsXznnTIusK2uxXmdCm6WbClm0bg9nT227fjX1Pp5euTMQXhsN7ZvEVTOHk+WOpT2c+BiYMSIzcP+5a2fy5sYCbnj6E75y/zssOLp/UM8jLSGWS44dqgDcg3y0o5gfuMvgTB92+HXwMlPi+X8zh3d3tXoc2+J+Y5frD7cVM3tsDLblDkfIuQ+8S3lNA5ceN5RP95Qxf0JO2IJs02ESVXU+vvrYB2y784yw1EVEwuulNXvwegwnjOzX5vYpQzMCt//vwsnMHZfTK4aySGSKmABrjHkUmAC8bK29Ndz1kfZ5PIZ7Lp4auN+RL+Hh8OBl0xn3k1fZcaD9rsCXPvI+q7YXkxTnZdH3TiQjMY7H3vmCb5w8otPr2no8hm13noHfb/F4DAsn9ueGpz9hza5S1uxqPVNfR43MSmHGyMzD7yhRYV2+swTV7WdPJC9Cf3Z6gsaAeuLofvzu/Mms2l4MwHef/DiMtTqocT3EZ66ZQV5ueJY5uuBP74XlvCISed74tIAZIzLbvXCfEh/DCSMzafDbdhsFRI6UiAiwxphzAK+1doYx5jFjzGhr7eZw10uiW0Ksl6zUeHa7Y1lfWbuH6jof8yY4Vw3LauoDX2o//PG8wDje/50/pkvnbWwtTYqL4fPbT6e2wU8wDSwVtQ3k3bqEix9ewb0XT+XY4X1JiPESH+s5ZJfo3mRrYQUPLN3KgqP6c+8bm5k1ul+nxmN7jeErUwc1m8nX77f4rMXnt8THeIJuHWu8kAFQUlXHzc+uZUdRFet3lxHjMVx87JCwdyHtyaa5rQXfPHkk2WkJ1Pn8gW1ZqfGH7brdXRat3cNK9/cOON13C8trQ3b8xDgvJ43O6lSvjWtnj2T1zhLe3XqAspp60jShk0iv88X+SmaPPXR34L997bgjVBuRQ4uIAAvMBp5yby8GZgGBAGuMuRq4GmDoUE2IIx1XUdPAv1buBAj8P2VIBs9/ayaL1+8D4Punjum2Sag8HtPhWVNbSoj1cs60QTz7UT7fadFq9KPTx3FB3hAykrpviaBI9uq6PSzdVMg/P3Te02dW7QKcMNBZdyzaSFyMB7/f0uBv3q900uB0bjh1LLHezgXN7QequPnZtQzpm8jT3ziBHz+3ltc3FgCQkxbPpccNU3jtZtlpCc26w/ZLOdiqMDAjka/NCk+37DE5KVz+6AeB+796qfWEKF319/85jpmj2u4G2JbvnzqWJZ/u492tB3h3y34WHD0g5HUSkchV2+Cjzucn7TBdgjWcSSJFpATYZCDfvV0ETGu60Vr7EPAQQF5eXphGLkk0Ou2oHJ5fvTsQXsFZu/XFT3bz8LLP8XoM184eFcYaHtpd507i2Y/yGd4vmcuPH0ZxVR33vrGF21/ZyO2vbGy17qzXYzh32mD2llWTlZrAPa9v5sTR/Xj4irxuabW1bkulz1r8fgItl8lx3sBMrw0+P2vyS/nta5v4vLCS40b0JaUDFwxOHN2v1Rdpay07iqq45m8fBcr6pyWwt6yGb80ZyTdnjyKmE39g73tzCzuKquifnkCMx+A1Bo/7/56yGp5euZOvPvbB4Q/Ujp1F1Rx/x+uB+7edfTQXH6MxzeEwc1Q/zpk6iGc/zqe6ruHwD+gmx+T25fr5Y5g0OJ2v/WUlPr9lypAM7jx3YpePXVpVz4UPrWDzvnJ2FVeRFBfDmZMHtrlvY6vvMbl98HgM4wc4awb/+6P8Vj93VXUNvP5pAQMzEkhPDP6iWZzXw5C+ibp4IxJhKmt9ACQHecFd5EiLlABbATR+E08BtMaBhMRd500iL7cvtzy/DoCMpFhKquoD4+COze0b0WEi1uth820LA8EKIDczmRue/gSA40dkNuue/Oq6vfzfks+aHWP55v2M+8mrfPKzUw854UJdg5/K2gZivIZYb+tuylV1DfxtxXamDOnDH9/cwvLNhe1OhDMoI5Fjh/fluY/zW217e/P+w3apLqtp4O/v7wBg8uB0stMSGJSRyIrPD7DRnXjmtKNy+OmZR5GdGk9xVR3ZqQmHOmSbbjh17CG333jaWDbtLW81IVBH1Db4A+F3UEYiL393Vq9tMY8UFx4zhGc/zqekqj5sdUiI9fLdU0YDzgUZgIEZCYzrn9blY1vrXDx69uP8wLj7V9buYePecjwGllx/MtX1Pv6wZDN/WvY5QGD87ZC+SSTGetv8Avvtf3zMG24Pgq6685yJWlpMJEI8+9Eu6n3+wMRNkbYkokh7IuWTugqn2/AKYDKwKbzVkZ4iPsbbbB3Yj38yn3X5ZYHuMh1ZIzbcYlusWXnu9MGcO30w1tpWLRlfnZHL/Uu3MGt0Px59+wtuPG0s976xhfW7y1jw+2Usu3FO4Hh+v2Xj3nLuX7qFAxV1vP/FAZr2oO2XEs/4AakMTE9ke1ElKz4vanau7NR4Lj52KF6PwesxeIzB63HWLf3nhzubhddfnzuJyUMyGJmV3KE1OAvKazj1/5bRJynO7RbcvGvw41cew5xx2U3q0vnw2hEZSXEcNyL4CbS23XkGSzbsY8rQDIXXCND4815SHb4A21Tjz9vxXfiMNWWMITMlvtmkcSu3FwdaW9/esr9Z9+Wzpw7ie26YBhiZnUxhRS37K2pJjPVSVlPPjDveAGBAegK3nDEBXxemb/7lf9Zz07Nrqff5OWvKoMBcBM99lE99kzHKHmM4c/LADs8CLyLBuf4p52L4A5c6HR8P14VYJFJESoB9HlhujBkILASOD3N9pAcZnZMCwKNfzcMYw8TB6WGuUWi01Q1v4uB0HrhsOgCXHjcMgNOO6s8dizby0LLP2VJQwZaCimZjauNiPIzKSuGsyQMZlZ1CfIyX2175lP0VtSzf7HzxHZaZxMXHDsVay6J1ezl1Qg63nDGB9KS2/9h9bdZwdhRVsa+sloVH96dPcufCW3ZqAu//6BTivB6eX53Pxr3lxHgMkwdncOpRwS1JFC7zJuSEuwriykqNZ+aoTK6KsGWJrpiRG7JjNf21sPqn88lIimPV9iLOfeC9ZuH15e/OYsKAtGa/R6rrfKzLLyPv1iWtjnve9MGcMalrY2P7JcdxySPv85MX1vOrlz7l1etO5I2NBdz68qet9t1fUcuNC8Z16Xwi0j7b5GLUL92x+E0nNBSJZMaGazG8FowxfYD5wDJr7d729svLy7MrV648chUT6QG2FFQw7+63mpUdPSiNE0dncf70wYzISmm2bdv+SjbuLSM7LYH4GA/j+6cFujA3nV1XRIK3eP1eCspruez4YSE75rr8Ur5079sAfHHH6RhjsNYy/OZXAvs8eNm0NidqeuK9bfzkhfXNyq6fP4YrZ+aSHBeDNwQ/9//6cAel1fX85rVN1PsOfv9Y8/NTaTz6+Q++R3ZaAn++8hiKq+rITFFLrEiofLyjmIsfXsFlxw3jkbe/wOsx+NzuIO/dPJcB6YmHOYLIkWGMWWWtzWtzW6QE2I5SgBXpPGstx9z2OvsrnBbV1647idHZKQqiIj1QWU09NXU+spu0phx72xIKymt56PLph+zFUFBeQ229nwOVdRyoqGXuuOxumXTpggff44NtzrCEueOyeezKYwLbrn9qNc9+dHAIwqxR/dhRVBXU+Lz4GA93XzC51UU6kd4q96aXm93/4YJxPPjWVspq6vns1oWthi2JhMuhAmykdCEWkW5kjOHtH87hzkUbOXfaYMb2Tw13lUSkm6QlxLZay/XBy6fz4urdzD9Ml/bG8eRD+iZ1W/3AGZbwwbYizpo8kF+fN6nZtkmD0psF2Le37Adg3vjOhenaBj/LPitk5fZiBVgRDnYbjovxUNfgjDsfk5PCz86cwPLN+xVeJWoowIr0EgmxXn5+1lHhroaIhMG0oX2YNrRPuKsR8KPTxzN+QBpfPSG3VdfkK2bk8llBBWdOGsiMkZl8uK2I7NR4hmV2btK9ytoGjvrZaxRV1oWy6tIDVNU1UFXna7Y+dChsLaxgRL/kiF0qqqzGWULs+6eOYerQPryxsYBZo/sRH+PlnGmDw1w7kY5TgBUREZEjqk9yHFfNansyLY/HcPvZB9fFPcZd6qezkuK8xMd42Ftaw92LN1FaXR9YEqtx9JR1Sw7eb7q9xbYOPebg9uzUBG48bayGaoTZXa9uZP6EnGYXcL5079t8XljJtjvP6PLxrbUs27yfdfml/Oa1TfzmvEmcnzeky8dti89v8Vt72JbSBp8zDCAnLYG1u0r507KtLDi6P+9sOQDA8H4pHJPbN+ifLZFwU4AVERGRHscYQ0Kslz+/uw2AlPgYYrxOmDQcnMndBPYPPLLZ/ZbbDaaNbc1Dan5JNQDTh/Vh/IBUtu2vYktBOWdNGYS3s61zBtISYiK2VS+S1dT7eGDpVh58aytf3OGE1eo6H58XVga2t1zzvDO2FJQz7+5lzcp+8MwaymoaSO2GNVX/8PpmJg9J5/5LpwfKrLX88qUNjMxKYWthBemJsfx+yWYA/t/MXB5/ZxsAL63ZA8C0oRnMG5/d6tgi0UQBVkRERHqk0iZr/r70nVnkHqG1v4sq6zjjnuV8/a/NJ538+X82BHW8iYPSOX3iAELRmJuTlsCZkwfy3tYDJLqt1MVVdYzJSeXdrfvZVVSNx13b22OcdXmNIbDet8fAF/urKKmu44SR/aht8DE2J5VIzNfFlc7733S+0n98sCNw+1cvbSA1IRaLxVpnln0L+K1z31qL3zqt6n73/tC+ycwZlwXAmxsLA8eaNz4HrwdeW7+PX70U3PvcEfkl1XzlvndIiHVaYX1+y4fbitvc9/F3tpESH8OEAWl8uL0Ia+H/zRyuiyES9TQLsYiIiPRIb31WyC9eXM+8CTn86PTxR/Tc+ytqeXrlLpLivGSlxlNYXhtYrqQzqut9/PPDHewsqg5Z3QakJ7CntCZkx4t0KfExrPvFabz4yW6+22Qd9FivCbSoNwZ1jzFuC73Tnd1wcFtNvZ+K2oZWx99y20Ji3G69u0uq6a5v1vvKarjgwfdo8FsmD8kgPsY55wdfFOEx0PjxeuzKPOoa/Owrq+XS44YS4/WwLr8UgKMHpXdT7URCS8voiIiIiEQpv99S0+ALybGWfFrAorV7qG3w4/Nb4mI8XJg3hPySajKSYjl94oBAC6TfbYH0+W2gNdJvLfU+P34LBWU1GGMoa9LSHWmueOwDThzdj79edWyz9ZA/+dmppCfGHuKRre2vqCXv1iXNym4/eyKXHDc0JHXtiPySauJjPG1OQFVQXsMHXxTxpUkDj1h9RLqLAqyIiIiI9DqXPLyCugY/3zh5ZKBL9/pfnBbUusKN9pbWkJUaT029r0vHEZH2HSrAasEnEREREemRUuJj2LCnjL+4k3nNG5/T5dDZPz0Br8covIqEiX7yRERERKRH2lVcTVWdj5Xbi/jm7JF8/9Sx4a6SiHSRAqyIiIiI9EhXzRrOsx/t4u4LptA/PSHc1RGREFCAFREREZEe6bzpgzlv+uBwV0NEQkhjYEVERERERCQqKMCKiIiIiIhIVFCAFRERERERkagQdevAGmMKge3hrkcHpAOl4a6EAM57EQvsD3dFJKp/LvrR8z5D0fx+RKNDfYb0XkSOSH4veuLvoUOJ5PciWnXlM6T3I3KE8704Ur+Hhllrs9raEHUBNloYYx6y1l4d7nqI814A09pbDFmOnGj+uTDGrOxpn6Fofj+i0aE+Q3ovIkckvxc98ffQoUTyexGtuvIZ0vsROcL5XkTC7yF1Ie4+/wl3BSRA70Xk0HsRWfR+RA69F5FD70Xk0HsRWfR+RI5e/V6oBVZ6hUi4WiTRTZ8h6Sp9hqSr9BmSrtJnSLoqEj5DaoGV3uKhcFdAop4+Q9JV+gxJV+kzJF2lz5B0Vdg/Q2qBFRERERERkaigFlgRERERERGJCgqwIiIiIiIiEhUUYEVERERERCQqKMCKiIiIiIhIVFCAFRERERERkaigACsiIiIiIiJRQQFWREREREREooICrIiIiIiIiEQFBVgRERERERGJCgqwIiIiIiIiEhUUYEVERERERCQqKMCKiIiIiIhIVFCAFRERERERkaigACsiIiIiIiJRQQFWREREREREooICrIiIiIiIiEQFBVgRERERERGJCjHhrkBn9evXz+bm5oa7GiIiIiIiItINVq1atd9am9XWtqgLsLm5uaxcuTLc1RAREREREZFuYIzZ3t42dSEWERERERGRqKAAKyIiIiIiIlFBAVZERERERCQU6mugcj/UV4e7Jj1W1I2BFRERERER6bKSHbDiQfA3BH+M2ASYdT0kZoDfD3+YDBV7ISEdbtgEsYmhq68ACrAiIiIiItIbbXgBVtznhE1M5x/v90FdOQw+BsafCdbnhNe4VKgpdVphFWBDTgFWRERERER6H7/P+f/6TyEuufOP37sOHpwJ1t+8PC7ZCbbWdr2O0orGwIqIiIiIiASrMag2/m8aW3MVYLuDAqyIiIiIiPRCjQEziO7D0EZQbXE8tcB2C3UhFhERERER6bQWwTfQAuu2ERZsgPKMI1uljuo3JmrH5yrAioiIiIhI79Oqy28nmZYtrS0C7F/PCrpq3e7aFZA9Pty1CIoCrIiIiIiI9GJBBljaGeva9HDnPhqZLZ1pg8Jdg6ApwIqIiIiISC8UojGqLSdxappgxyyA+JTQnEcATeIkIiIiIiK9Uai6EB88oFveJGJ5vMEdW9qlACsiIiIiItJph5nEqeVtCYmgXlFjTLoxZpExZrEx5jljTJwx5lFjzHvGmFua7Bd0mYiIiIiISPcJ0TI6rSZxanI8BdiQC/YVvRS421p7KrAXuAjwWmtnACOMMaONMecEW9b1pxVGL10PL34XtiwJd01ERERERKTbtDeJU9MWWHUhDrWgJnGy1t7f5G4WcBnwe/f+YmAWMBV4KsiyzU3PZ4y5GrgaYOjQVOsvwwAAIABJREFUocFU+cjZ8l8o2w2lu2DUvOCO4at3/sUlhbZuIiIiIiLiCDTABjsLceNxDjGJU1ePLa10qU3bGDMD6APsBPLd4iIgB0juQlkz1tqHrLV51tq8rKysrlS5+123FgZOBesP7vF1lfDb0XD7AHjnD6Gtm4iIiIiIuELUhbjl8Zq1wCrAhlrQy+gYY/oC9wLnAtcDjQscpeAE44oulEW/6iL44zFQU9q8fOQpcPYDh3hcCVQXO7eLt3Vb9UREREREJBRatMAqtHarYCdxigOeBm621m4HVuF0/QWYDGzrYtn/Z+/M4+yoyrz/O7e7kw4JSSAJCRAhIGDYBSOCgDQSFNRRB0cQHDTvjIIbLqPijNswvOI+fnT0VcyIjDoqwiiLOLIoZBEJSZCEHQTTkA5EsocsnaT7nvePunW77rl1qs5ader28+UT+t66VadObafOc57n+Z2Kw4DNzwLrnwSmHw287Nzo35gJQP8f1IvhjuamIgiCIAiCIAiiFVfT6LSJOHWGPy5UTD2w/wjgRACfYYx9BsC1AC5mjB0A4FwAJyO6gosNl1UbxkZu5Fe+F5j9hujzje8H+hfnbMwlnwmCIAiCIAiCCAeZiBN5YH1iKuL0PQAtcbCMsVsAnA3gq5zzLY1lfabLKk+a9zRp2Kps58IDyzmwaRVQHx5ZttcUYK997csmCIIgCIIgiMrieBqdNBEnwjnGObAinPNNGFETtl5WbRhS54ECkO9VTfl9eAioDwE9vfpVWfFT4OYPti7rnQRc3g/UKLyBIAiCIAiCGOW49piSB9YrZMH4oMXTmryBFTywLfBI1OlrhwJXTQfu+75+Xbavj/6+5bvA264BjnprJCzFh7O3IwiCIAiCIIhOxjraUQgh5pQDWwTOPLCEQDyNTnIERmUwRgwh3rFhRMnYRpX46L+N5pXduAp49CbzcgiCIAiCIAiiIyARpypCBqwXEiHESsuTZPy+czOw8KvA0GD7b3vvD7zyPRnzUbGRKgCkcEwQBEEQBEEQVkhEnCgH1itkwPqAsREPbPIG1hVxEh+Gp+4Etq8DWFerocrr0b8j3wzsPT2jvJYfsutBEARBEARBEJ2MrUNHJuJEObBeIQPWF6k3sKYHlqP1waoPRX8/tAyY8tKR5cuvBW79aE5eKxP+EgRBEARBEMRox2XfmEKIi4DOrhcyDFVdEacWg7YuXVNethhCLMbqEwRBEARBEMRohEScqgidXR9khRDnkWVYNoWNhXKyym1TQ5bF6hMEQRAEQRDEKIJzu3BfmYgTRTx6hQxYXzRHYJILVUKIhTJSc2LFhyLLKHXhCSYIgiAIgiAIohVJH5xyYL1CBqwv0sJ9jUScUkKIZR7YrLLFEGKCIAiCIAiCGNVwWHlLpSJOZGL5hM6uD1jS02oh4gQI88Lm5MCmld1cRCHEBEEQBEEQBNGCU+cOqRAXARmwXsjIe9UJ3eWiBzYvhDi1kIyyCYIgCIIgCGKUYt0flog4UQ6sV8iA9Y6NiJPojTUIIRan86ERIYIgCIIgCIJo4EHEqRlCTP1uH5AB6wOpkag7D6wLESdxHQohJgiCIAiCIAh30+iIi2PHEZlaPqCz6gWJ11VbxAlQEnGSbpvYnuaBJQiCIAiCIIgRXE+jI4o4kQHrBeOzyhibzhhbnPh+DWPsXsbYZ10s6xx0RZySCB5YmdGpNA9sStkEQRAEQRAEMarxIOIE8sD6xOisMsb2AfAjAOMb388D0MU5PwXAoYyxw22WuTiwUpEZlIwp2I0ZBmtThdhgHlgmhhATBEEQBEEQxGjGk4hT0wNL/W4fmA4LDAO4AMDWxvc+ANc3Pt8B4DTLZS0wxi5hjC1njC1ft26dYZWLRFO4KUlWCLFMmlsnLJhCiAmCIAiCIAgiwmUIsSjiRB5YLxidVc75Vs75lsSi8QDWND5vBDDdcpm4v/mc8zmc8znTpk0zqXKJ2MwDK4o4pZWZV5y4PYk4EQRBEARBEIQ7h45QDok4ecXVWd0GYFzj84RGuTbLqk1mCLGGiJM4D6x8h1kFpv9OHliCIAiCIAhiVCPpJ6siE3Fqm/2DcIkrY/F+jIT+Hg+g33JZxZGFEFuKOKWWifywYFEJmSAIgiAIgiAIy76xuK04+0f1/XIh0u2onJsALGaMHQDgXAAnI7qCpss6CM1pdIw8rhlhwVwcWSIDliAIgiAIgiCchxC3iTg5Kp5owWpYgHPe1/i7FZEY0xIAZ3LOt9gss6lTENh4PFVCiE3mgVValyAIgiAIgiBGEy5FnITl5IH1gisPLDjnmzCiJmy9rNrk5aRmYSDilDcPbBkhxMN7gMduAfbsNNu+Zxxw5JuBrh639SIIgiAIgiAIJ0im0aF5YL3izIAlErTlvSaW64g4RQs0dixbN60+nj2w/X8A/ucf7Mp4183AoX0uakMQBEEQBEEQ7biYRqdtOU2j4xMyYH1jI+LEefrqWiJOkpAG3yHEQ7uiv+/4OTDjGL1t1z4MXHfhSBkEQRAEQRAE4Zo2rRhdcuaBpSRYL5AB6wWLaXTEEGKd8mUiTqmjQ75zYBvlT9wfmHyQ3qbb1zeKqLutEkEQBEEQBEE08SXiRCHEPqGz6gNZCLEKRiJOeR7YEkZ/2ubB0qAoLzFBEARBEAQxunERQix6YCkH1it0Vn2jHUIs/K5ixOU9eGkiTr6Nw9h7atIoxA87eWAJgiAIgiAIX1j3hyUiTs0cWAoh9gEZsF6wCSEW0ZkXVjYPrOK6TrHwwBZWR4IgCIIgCGJ0QyJOVYPOqg+kIcQKHtiWFFjJutrzwJYYQmzlgSUDliAIgiAIgvAFt+wmy0ScWOtfwikk4uQbMXxXV8RJax5YBRGnwvJLXeTAUggxQVix9D+BLavLroU5R70FOPAVZdeCIAiC6FSc9YdJxKlIyID1jqagk8o8sDIRJ606+M6BtfDAUggxQdgzuBX4308Ate7oX9UYGgQ2PQOc/6Oya0IQBEHkwTkwuLl1WXcv0DOunPpo4VHEiabR8UIFezUVINNo0/DAcpkHVrI/nVGkoD2wFEJMENbUh6K/r/sCcPL7y62LCd89BeDDZdeCIAiCUOG3lwNL57cu6+4FPvIgsPf0cuqkBIk4VREyYH3T4vzUFXGSTKPTZhQahBD7xioHlkKICcIa8SVaOUxE7wiCIIhS2PwssPcBwKkfjr6vfRhY8d/A9nWBG7BwM42ObHll38FhQwasF2xEnCymzZFuW0IIsQsPbJkM7QK2r89fb9w+wJYB4OYPAEf/LXDKB/3Xjegc/rIA2Pq8/Pdx+wBHvN7w5VpxA9ZItZ0gCIIwpv8eYMNT6b/VuoDZb4zeS2lwDkzYbyTi57FfRwZs6OlgrqbRaRYjemAr+g4OHDJgfZA3GpOJSgixTMQpp7zkut7ngbXpPAfggf3R3wCr78tfb/JBwJmfBQaWRYbIKR8EHrkJ+PMd0bGf8iFgv9n+60tUj8EtwI/fityX+4eWA1MP1y/fZi7mIFCZN5sgCIJwxs8uAHa/KP99x8YRD6sIr6fPwlGJaDoX70khhBjkgfUJGbC+kU6pI6HFsJSFELftJLF+SnmpHdiCOoZWIcQldl5ffB6Y+UrghIvl6zz2a2DVwkTj3KjvH/8D+OsjkQjNxAOA/T7tvbpEBdm9AwCPBkCO/bv23/sXA7dcBuzaalZ+876sqAFLHliCIIhiGRoEXvle4LSPtS6v7wG+dXwUnSaFtxprldEzkfWTFZGJODXPRUXfwYETjAHLGLsGwFEAfsM5/0LZ9bEjx2iVGpWAkgdW3DbT4BPngS0qB9ai85w1LVBRcA5MPQJ4xbvl62x+BvjL3e1CM/Vh4JAzgKd+Bzx6C/D0XeYN+PSjgTf/h9m2RNgMNzoCE/cH9j2k/ffNz0R/MzsMGTQ9sBUd/WWePLAbV0WDT2W2L0ecG4XhTZhWXh0IgiBEeB3onQhMOrB1eT3u52S0m7yOlj5fVfRMXIUQ0zQ6hRKEAcsYOw9AF+f8FMbYDxljh3PO/1x2vYzJCyHONGBFVEScstYVVi88hNhiGp0yG722UJgUWC1q1MV68nr0G6sB6x4DusYCs07Tr8OGp4AVPyUDVpX6MLD+Sb37ZuKBwLjJ/uqUxfCe6G/X2PTfu3ujv3t2mpXfESJOHtqAP/4HsPyH7svV4c7PR3/fd080SGXqZU+jexzQPcZdeQRBjCJEp0eMQr+MSzywlUgF8SHiRDmwPgnCgAXQB+D6xuc7AJwGoLoGbNoIVMvyrBEsMYQ4rXiNeWClhmpRIk4GhBBCHBuhWbAuADwxMpnYttY1sv2E/YCLf6Vfh7u+ACz6evT5l+8F1twflXn2v0VCCkQr935nxDDQ4aBXpy/v6gZe+/norwmTXgKMnyr/PfasdvWk/949tnU9XTrBA+ujDRjeDUyYAXz4T+7LVuHq04CNf4k+b/sr8MB/A/d9z135e00B/umxkfuHIAhCFdEIjVHpl0lzYEM3YC3rlxdCTBHEXgjFgB0PYE3j80YAJyZ/ZIxdAuASADjooIOKrZk1aeEUWQ+LhYhTarFlhRDbTKMTwKidkgHb+D2ebzM5QsmY/ehbbCBzDjx+KzBpZhT+uGoxsPcM4Ib/E3XGk/TsBfz9L9NDUl2y60Vg8+r037p6gCmHpV/7P3wTeO6B/PLHjAfO/r9ArSZXPBR5cW3kfTrv+2rr3/8j4OnfRwMDLzmp9bf6ELBqEXDNXLWy0phyGHDZ/fLf42snMzRiD+zQoNn+ScQpHY5ogGnMePdlq8C6Wr9vfgbYe3/g1ZfZl/3MH6O2Yvd29wbsjo1R+TbstS9wsGTAiCCIAJBECDKG3KgYsd9UmRxYOHpPkohTkYRiwG4DMK7xeQKAlqvNOZ8PYD4AzJkzJ/wnQTrvqq6IE+BcxKkw76bFNDohhBDXh/MbnZpowArb1hodVdPGK96+Phz9e9m5wIqfA0M7o/nVNj8DHPN3wJi9ovW2rwee+N8o9NjEgP3z74AXHo0+j58KHH+hvFH/6duBZ++Vl3XeD4Dj3t6+fNHXo+OakDEn3NDOaD65/j9Ex3jpYmD/4/Lrv2cnMHZv4Ki35K8LAM/eFxmwE/cH5t3a+tvgVuDLL4k+TzkceN3/VSszZtk1wHM5Hr7YgJV6YGMDdrR6YGue2imJh6EoxH1zDoyf5mYKrlp3ZMACwPAQcO25wJbVwL6HAu++daTNMuHuLwLL/tO+jh99KFJvJwgiLETDS4SppHWw9o+VyIF1YMC2eWDJgPVJKAbs/YjChpcAOB7AE+VWxxbZwx9/UO2UKYo4NVe3NHZd4sIDG3wIceP3OJexZdtECHFN8Lio0jwPw9E/1gX09AJ7BkeEo15/VeSNBYA1f4oMWDGkWZVf/iMwuHnk+8GvBvaZlb7u9vXAzJPaO927t0dz4u7YkL5dfSgSxnr9VfJ6PLcCmH/GiIjRltXqBmxPb/56MbGHSvSIAa3XbOL+0eCBDk/9HlizPHudpgGb54E1zIHthHlgvXhgBaGRohHbRJV8e/XCR8rc/SIwsDSKynjmnkg0rDYue/Ms9uyIBp7e+T9m2z99F/C7f40Gd154NBp8O/4C4NklwHXvbG1Hx+wVGdxTDzOvL0EQeuT121gNuSJOLe+bAAQ5lXBRv8T7qjkOQAasT0IxYG8CsJgxdgCAcwGcXHJ9HCJRIZYihBCrlJk7D6xuHVxg4YENQblOOQcWkbx82ra2jVdsRPF6ZJTWuqIQ2aGdI0Zq0vhqrm9owA7tAl71PmDGscDNHwSGdsvX5cORF+Xot7YuH9wSGbCiVzq5XS2n2RF/370jv+5A1MHu2UttXQDoiTvzaYNEifOqU2ZMrQuo59y/8fntkgjuWOfAVtyA9SXipCWi54G26+HQI5yMsImv/15TgC075M+kKrweDbaoDCalsfHp6O+ffgTs3BQNOB1/AbDucWDHeuCEvwfG7A1sWws8ciOwaRUZsARRKHnvjLwQYt4a5dEchA/cAwvYvxNSw65pGh2fBGHAcs63Msb6AJwN4Kuc8y0lV8mO3BBiHREnFQ+sQQixb6w6zwGM2smEDJI0c2BFEafhVhEnqxxYNDqePDLsYg9svM+kp5AlQo5N4MOR1y822LIM4digFomNT9m2su1ayhB+37M9e/2YocGEUapAbCAOp3Tsk3XQKTOGdeUPJDRzYCUGbLxf2xzYqr48fYk4OfV4mpDigXV+jRLnLQ5RtzZguWU1GxuLAzvxNT7zM9G82QPLIwM2ee3rw8B1F8nz7rMYPxW46Hq96AyCGI3kvTNYTSEHNmFWhCDIqYKT+jFg2Q+AR25KvLPJA+uTIAxYAOCcb8KIEnHFUZhGR4rwm8qD1bT3ApoHVswB0KEqIcSxkZMaQlwbMSjTQlRVEEOUWcIDGxtHyTraemDrQ5EBWlMwhOMw6bY6J41ucRvu2QO7Mzo/qsTrih50wIEHtpY/kBDPAysLIe5ypUJcUQPWl4hTiDmwzjywibYzvv612IA1bBeaWOaJiZE1Yr5Ys+yUAcxdW4EnbwP2O1ovv3/LALBqIfDic1EeMEEQcpqOB8nvuboEQhtRJQ+sbd+4719GNEQA4NAzRsQhyYD1QjAGbOdiIeLEJR5Y3XlgWzYtaEQsTwwgi8qEEMceWNGAbXRIbT2wTQO54amr1SIvwu4dCQ9scrTTwgMbd3hrXdlGaEx9OF0QJq5PWvhs06Dy5IHdsxPonaS2LpDwwKaESiePzZsHNp4HVuKBrdWi30brPLC+RJxKz4FNfI6fO1eDDMm2kzeuu1MPrM29FNctfi4Exc5me5nyjoo/n3gxcPL71Xe58jrgxkuj7dc/FaU4FMm0lwFjJxS7T6I8bLQ/giDnnZEn4lTlHFjba3bGJ9uX9d8T/a3s/RA2ZMD6QBayy3QfZi6xX2UhxGlFyB5M3wasjfcngIfdRsQpViFuijhZhhDHBlbsgd2xYaQTmDT2VDynMpLGZV4YcPxbmie1lmH8No3uPM+2hQc2FrRSIRZJEq9fsh71ITMDttaVPwATe1ZlIcRxHUetCrGndiCoHNjGIKWza5R4x8TX35kBa2loi96YuLMvvisyBzA19x/vc1M/8N/n6W3rguMuAM6bP/LdZkCGOsFhs+Rq4LZPAQefBvzdD6Pcbh/sMwvY52A/ZSuFEOekwLX0eQOIpiuTqr+DA4cMWC/kvGiUQoizypCIOIUYQmzkgQ2g0TOZBzbZ8XLigRUM5FpChbi5z2QObEK1WJe4vFpXwgjNMMDqQ5IQYgaApdeBp3iN0xDL3aNowA7t1DM2e3IM2Ph8moQQM5UQ4hwRJ6BhwI7ieWC9iDgpPNteSVwPXnfrEU62nazRftYcGbCuQojrggd2ZAXhb4oHVvu6NcrauSn6+5pPRurpRfDby6PpuGKGh4BvnxBNEaZL11hgzj+48+bOOj0KcUxj5XWtoZBp1Lqj+kyaGX1/cW2kJh0qM+eM1DXJyuuAvyzQL+9lbwCOenPrsvWNyTP++jBwwzzgWcs5k2VMOgj4wL1RZEEyouAV84Bj/86ubNcqxCFE06ngahqd9oKjP67n5CYAkAFbAIYiTs2RLksRJ9m2RYUQG+XABtDomXhgmx4FcR5Y0xxYIce21t3IgR0cMS5deWCTolAqhnCWGFPsuWzbJsXolm2fZLdGCLGWiFPDgE3LgQVGzrtpCHE8DZbsGSjMgK3o6C9j+UrORvjqrCiSvB9itWDnKsRpIcSWObDOQojrwl/hXZEaQmw4GCOWdcCJwBGv0yvDlLuvan2HxfNbv/S1wEtepV7Ojg3A0vnAfd+LvtveK7weTWl06F3pv//6I1HbJ5ufmvMof3/sROC0j0bL7vgc8FDAEiYvewNw4c/bly/+RjRN2/ip6mVtewHYuKrdgE22t7teBF5yMnDW583rnMZ93wNWLQY2/Dma73nakcBe+0ZTz42daG/A2qoQy3JgqxBC7IP9jgYO7QNec7mf8kc5ZMD6IC+EWMUDa2Jopq1bVgixi2l0SlUh1hBxSptGp9aV6JDZ5sA2Qkib88BKRJxiw9DEA5v0jmaFATfXl4g4xfVO6yynKSenbm9qwO7QFHFqjIpKj7Nx/5lOowNEx9wlaWaVDNix5gZs1eeBBeClDXBpMJoghhA7DWkOOYRYyIHVEXEyvQ/EfRYZjSB6q+Lrcdjc9vmzs9jwdGTAAsBpHwPmXmFXr59dALz4fPpvnEftzWsuB177mfR19gwCV01vfc/s2QHs+1Lggv+2q5sPbnp/ZFCmwevAEa8H3v5f6uX95G+BXdtSykqExPN6ZBTPOlW7upk89mu0PNtnXxkNyMw/M13LQZfcEOIcZXhZDmzoHljAT9swfgrwrpvdl0sAIAPWE7KQXRURp3hVDQ+syTywvrESMwig0dPywMYdw8QLzEUIcZsHtpbwwMZ5tonza+WBTXhHm0JMNh7YtBDi+sjvWbSJOKnmwOpOo6O4rpEHNunFlhxvPA9sVnjRqM6B9SjiFMo0OrwOOJ0HNuAQ4rZtRREnYcAvTcRJ+7oJYctFPgui4I1pGHRaTqFVvTKmQonPk8z7mqyDeGw9ewHTj7Kvn2t6J8rnNDdKJ5B4IZvLHAuztey6NhK1EX8HokFQJwasQghx3jywqTmw9lXzCgdKjcohjKhoz6ai6HgWtfJAM8qVzQNbWH6pTQhxSa1ePHCQa8AKnsq4Ya83vJNNESfbaXQSIk6xBzae8iatPiaGfz1hXKp4cuMw6dR6SxR4m0ayomc7RsUDWx+OPNU63lLVvBRbD6yMprp0hkHf4yCEuKovZsbgrecTigc2Od2Nk7KTHtjGuQtFhTht+qDoQ+N3IYQ4zQNravyV8SyInX3TOrRE2bgyYCW/1RPpKnn1abk8ZQ8KZZBl3JkYsDIjjicGZHzl2ceDImJIfVePXMtBi5zIuVwVYqGNCCEdjOhYyID1gSyEGCqGWbIBSa4nhlel7M/W2HWJVQ5syXkTqiPl4jQ6sbHSNg+sq2l0GjmwfDjyyokhvC48sLVE7m6miFPGfK61rhwVYs15YFU8sLGRFwszqdCtuK6VBzbjHA7visRZsp6R7oZolwlVn0bHp4hTMDmwdcchzckcWCHiwToH1lEI8UiBI+VGK7T+dWH8iVP3FPosCOGWxh5YxwYsIH+u8qb2StZBvD6hGrC1HrnOgUn0g9SATeR2ezNga4IBm/DASo9Rg7x7NE/EqS0HNoB0MCVcpnEQRUEhxF7ICxtWEXGKX37i9yzSfpeEfXn3btp0nksetVMNvawJIb7NF9hwdL2sQ4iFEOVa14gxtXtbu6fSRoVYOwc2K4RYkgObNvVP6vY9rUJQ214AHr0le5vdjZwkHW+pqrFrLOKE7GsxvCffC9w9FtixUX//QPUNWKU2z4DQptHxMg9sIoTYlYhTtAN322qJOBkOiDYHGWMDVm9zK9oMHcsw6LhMW7LCQJsGbFYIsWSAIdR2pqsnkeYj4NQDmxQn85Rn3+aBdR1CnCeWlueBFduyinhgR+s0PxWHDFgfSJ99HREnMYRYDK9K2WGqiJOwTVEdNxfz3ZUWQqyoeBlfo83PtG7XFHGynUan8Xj+8h9GvsdG164XMzywBqGCzQ5eV77x1QyRkhiirCuaSmDJ91qXx1NZ5KkQd3UDf/+r6Lw+/hvgyduA6y9WO44J09XWAzQ8sOPVy4xR8YYP7cruKAKOcmCrOrLsKYS4dG9R0gMbD1K6MmCT4cmxiFPDm1Z6CHFKedEHYYUOCiFOE3EyNcLFz6Zk5sCqhBCnGSUlC6Nl0dWTEUJs8OxJc/OLCCGWeWBdhRA3d5S9fxmVnge2qu/J0QsZsL5JG41S2i5++SVe3Hw4vYxcEaeWlSXLXeMghDh0D+z4adHftQ9Ff4cGo3nlhvegRcTJNAf2kNcAr3p/NP1Cdy9w6JnA07+Pfnv0JmAvQfo/7nQ888fsELA0Yi9fch7Y4d3pI9d5ntR9ZgGrlwADy1J+ZOnz8YnEcxQe9w5g/ZP56wPRMU89XG1dIN/7ecFPo8noDzhBvcwYlXzk4d1RCHEWo3keWF8iTmV3tkUj02lnN2lcxO1PHELsQoXYYvu2YxRFnIQBvzQPrHEIcQmCZmIEgWkd0lRdbcgKA1UJIU4rI2gPbEZ4rZEHNkfEyfXczi37zgghdirilBFCrKNCXJkcWE72awUhA9YLGQnwAJRCiMX8mebLPcMDKy23hBBiqxHvkvMmVDsah54B/NPjkYHxpx8Df/hGNLk4AIzfz94Du9e+wLlfbl32snMj6fyh3cCMY1p/6xoDTJgReSyfvM1ghw3jMjaqbpiXvbrMezjvN8BuybQFtR5g7AT1KnWPaT9OV8ThxntNSf/9yDdF/0yoNa757u3AGIkHd89ORQ/sKJ4H1pcHNpQcWNfT6LSEEDeWuRJxcq1CnCvilFzX0nvZjDApU8TJ0AhPMwhsyBLiaU7tldMupQlUhdrO1Lrl3kmTZ082AFB2DmwRIcS5aR1iG1GVHFiiipAB64Os/AEgmihc5nnZ+lxi1YSSZLMxzGhsVeaBLTqE2MoDW7IBq+I5nbh/9PeszwOvePfIi2vywSMTu9dyOgM69E4CTv1I+m+1LuCjD6pPO9O2fcO45Bx4w9eBnZsz1u0Cjr8w/beubmDcPmZ1KJLuscCbvx1NNu6a+Pn+1nHZ6007Mvv3UT0PrC8Rp7JzYIUQYped3RYVYjGE2FbEyfK82Yg4mc4r3jYPbMHPgmsPrPcQ4lhvIa9rKDybIRuwWeG13jywKMiAbdzfzlWIc/Yv3Vz0wJYcTaeKSSg5UTpkwPom+dI+4ARg6hHA8w9mb7P/yzHSkCgYgpme3bJGxExDvjByPM89APzpJ85qpExsMOi8gBiLQmeTvPZzwKpFwGxDL54J3WPVp4eRwRhw0nvd1Cd0TnyXn3KP/Bsx1h5wAAAgAElEQVRg19b8UfGZJ2X/3t0b5Q5ffbp+HWJhq6q+mH2JOJUdQuwzB7bF+Gt8dhVCbH3eJB5YLREn3f2XnAPLE0aFsQEbmIhTWhkhGwBdY3IMWJMc2IxpdOB4UErcNzAyQ0DhIcQ5UTGlT9tIjCbIgPWCJO/1oFcBH0rLC0zhx2+Nwg91RJzSEBuOohoUKw9sV5Tf+fit0b+y0BEESuOws6J/xOhj3GTglA/al3P0W4FN/eYj2Ae+AphxrH09SqFTQ4hFFWKHBnWmCrGDHFib8ya+C6QiThCWJz5bhxAXnAPr8hjEz6aoiDip5MCKBmyoHthat+McWNVpdDzlwALtc6pnGek6OFEhTrtfQzdgS47KIYzQNmAZY9MB/A/n/PTEsmsAHAXgN5zzL9guqzzOQnYFEafog3x/MqM0tQoBe2BrNeAjK7JDWH3T1QPsPaO8/RMEALzkJODCn5Vdi3JgNT/NVNmd7dR5YB17YFNDiF2oENsYsCWIOLWFEJeZAxuIBxZM/lzFRlBe2oso5hMr74dIpnFn0BbkGbAAmlPpuSYus2nAJkOIHXhg8/pteSHEsog/CiEmPKBlwDLG9gHwIwDjE8vOA9DFOT+FMfZDxtjhAI41XcY5/7Ozo6syYuOXKeIUoxNC7BkbDywAjN07+kcQxOgkS2zGhhCn0XHmgU0af402uOZQxMlHCLFUxCkthFj3uomDu1U0YF2LOKmEEOd0DdMMWOZQ68ElXT2RBzZtAMapBzZxPupDnkOI0zywIaoQV2kaHaJq6D5hwwAuALA1sawPQEOtBncAOM1yWRuMsUsYY8sZY8vXrVunWeUycGEwNvK/mg2Kwv5S7deychJK6DAQBNFheGqnQptGx1nZCY9H0wMb58Daijg5DiEWPbAqIk6m3ssyQojFmQScqBC7CCHOGBiqq3pgKyTilDWAY3JPSwcAkgbscPEGLK87esYhHyjJG1TkKKm/aQuFEFeRzCeMMfZ9xtiC+B+Aj3LOtwirjQewpvF5I4Dplsva4JzP55zP4ZzPmTZtmuqxlYeLEGLGgC2rgQd/ES8Q/kr2l19w429RObCBvtQIgggbXyJOpefAJvftOAe2pX2Pc2ADCSFu88DGOYMqIk6GRn5QIcSmRniBObDxvN+5ObCiIROwARDngKd5KE2ePZkXsgiDXpbTnXWMWiiEEGeKOFU1B5aoIplxIpzzSxXK2AZgXOPzBERGsc0yAgD2Px7ovwd4+m5g7wMiUZjBzZKXhI4Kcby4IA9sqC81giACx5eIEx+Zp7cM0jywzuaBDTiEWNxWKuLkIYQ4BBEn07lsk+9vVx5Y2XPVnAdWIYRYPLZQB6ubxl1KHqxRvSVeyOSy+hD8DJKJObAJDywQXb+ece2bqZL7nDGg/w+RyGga29chvb9JObCEe1yoEN+PKPR3CYDjATwBYMBiWQfgIIT4rM9H/2KufaO8vKwwjdLmgS1h2gKCIDqHvHwrU3gdYGUK8PvMgU0Jv+1yNI2ObScvL4S46YFNy5uzDL+NPbBl5sA6USH2nAOrHEKckt8brAGbEYFgElUgiwwRDdhCQ4gzjHQtcqIEjjsfePL2aIaMNA54OfCyc0a+x+W8uBbY8LRl3Tyy60VytlQQF2/xmwAsZowdAOBcACcjegpMl1UfHwZjm8BFGjIPbEtBGes6pNnfoEaBIAgDfIk4OZ131YC2aXRchjRnhRDb5se5DiE2EXGyzYHV29wOMQfWhYhTIPPAil7IkA3YeB7k1BBiH/PAwn8OLBdDiBMeWBvyHA+v+UT0T5XYG/y7f43+hcz0Y8quAaGJkQHLOe9LfN7KGOsDcDaAr8Y5sjbLKs/hrwOefxCYsJ/9XKIxB54YlXngiSk/5jXAKb+vXgrs2OSiZulseEq+b4IgiFx8hRCX3NlOnUbHtQpx838BhRDLPLCiAZ/iRbYNvy1Dk0H01JlGJbk2YLPm8ozvkTwDtooeWFchxMrT6BSZA+vKgHWc+rX3DODiG4Ht692U5xMyYCuHkzgqzvkmjKgJWy+rPEe8PvrnkrOvjP6lEbc1t34MGDOh9betzwF7J4zo3knR39v+2W390hgzIdyXGkEQYeNNxKlkwZkWA5Y7zoFNGn+Ntjeen5M7UCh1Oo1OQsQpV7m0A0KIXYg4uah/Vmh+bABpzwPrqG4+cJ0Dq2LAAn7aGO8hxMJ+XPDS17oriyASlJkI5Iw9e/ZgYGAAg4ODZVfFOb29vZg5cyZ6ejJeKPsdBZz4bmAwxYG935HAoX0j3w86GXj/H4HdO1xXtZ29Z5QrlkIQRIXx5IF1mXNqhOCBdVqfgFWI2+bgTIYQp+lGOBBxKnMaHamIk2EYtMm2qeVlGbCKIcSpHthADdg4hLieZtyZqhCXZcCKIk6N765UiEm7hKgQHWHADgwMYO+998asWbPAQm1EDeCcY8OGDRgYGMAhhxwiX7FnHPDm/1ArlDFg+tFuKkgQBOELnyJOweTAwm19mrYfBxDPA+swhNiXiFOakZbmgTVRjAUsQpAtSDPyTOrgxYDNCSFWmge2KirEGeG1Tj2wvH0912TNAwuEF0JMEB7pCAN2cHCw44xXAGCMYcqUKVi3bl3ZVSEIgigWxoChXcDah9yWu3sHMD6UHFjesAsd58CCjzj/mjmwLkScXIYQJz7kKe3a5o+KojeFIOaaOsjDdTWNjq2IU6VyYGPvZJoKscHgkTS1oUwD1rUKcWf1pYnOpCMMWAAdZ7zGdOpxEQRBZNKzF/Dic8DVp7kve78j3Zepyr4vHfm8aiGwa4vDDmPC4xh3bmu16LN1CLFlmGhbCHE98TdPxMlRCHHhObAORJzEMm3JNGDjeWBVPLCCcR66ASuGENvkJCuFEJcg4rTm/mjQz5TNz8Y7Mi+DIAqiYwzYqnLFFVegr68PfX19ZVeFIAgiHF77OeCws/yUPfOVfspV4azPAa++DPiPE4DHb42WTT7ITdktAkgJg6nWXX4Icdu2shBihyJObSrERRqw4lQzoXhga5DmliuHEAtlhOyBrUnyQ6tswN7/X/GC6M+4faO/rsQ5x050Uw5BeIQMWIIgCCI8xk8Bjvybsmvhh3GTgY8/DuzZGX2P1eFtSQshZjU3BqxtCHFb7q9MxAmJ5cJHUwXfMkKI24w8FwasKxXinBDiWLlaXkh1RJxk0+jYiGoN7wIWfa11+ZY14op65aow61TgZW+M9v/SsyKhTCDSNblkIbDrRft99IwDDkibrpEgwqLzDNjf/rP7nKkZxwLnfjlzlcHBQcybNw8DAwOYPHkyfvazn+H888/H9u3bcdhhh+Haa6/Fzp078fa3vx1bt27FlClTcMMNNwAA7rzzTnz+85/H1q1bcdttt2HGjBlu608QBEGERffY6J9TUkKIWeyBdTGNTkkiTsYCSI2/pYUQp4k4WZTZM86qSs0KZIUQd43JP8+sFq0bz2ZQ9zTvqQu6Gt3cF9e2GplxSLHu9ZhyeDQYdNcX2n8bOxHYtTX63D1Gu6q57HsocOHP2pczBhzwcvf7I4iA6TwDtiTmz5+P448/Htdddx2uvfZaPPbYY7jsssswd+5cnHPOOfjrX/+KgYEB1Go1LFq0CLfccgu2bdsGAHjqqaewaNEiXHnllbjrrrtw0UUXlXw0BEEQROWQhhB3hRdCnDRQU0WcXIYQlyXi5GAaHQC4ZEE0p/thZzuoViMfWvQgAsCzS/LDh4HIyH305uhfzKzT7evmgzETor83fyD99+5evfJe8W7g5ZI+2tAu4JFfRef3yLfolUsQhBadZ8DmeEp98fjjj+Ntb3sbAGDevHno7+/Hl7/8ZVx77bXYuHEjdu7ciRNPPBHHHHMMXve61+Hwww/HOeecAwB417veBQA46KCDsHu3pQw6QRAEMTqJjaNbP5pY5igH1jqE2IWIk+4+RdGbgj2wQ4PA6qXR9xceaa2TDgecEP1zwZTDALB0DyKgFj76xn8HBpa1Lpv9JuuqeWHqEcD5PwZ2bm7/rdYNzH6DfpkykauuHuDEd+mXRxCENp1nwJbE7NmzsWzZMpx11ln44he/iO985zv4+te/jvPPPx9nnHEGAGDlypU49dRT8cUvfhEXXXQRFi9eDAAYP358mVUnCIIgOoHpRwPHnj+SC3fgK6IUmFo3sOEp4OFfmZe9czMwaab59m2Gm4GIk0m+IjDigS2SsROA7euAawSvaewRLIuXXwgc+3ZIhZxYXv4rolzMWac6rZY3GAOOIm8oQXQaZMA64r3vfS/e/e53o6+vD1OmTMEvfvELfOADH8DVV18NAFizZg2OPvpofOpTn8JVV12F3t5ezJkzp2nEEgRBEIQVvZOAt/1n+/LxU4FVi6J/NhzyGouNZSHEYmhySgix8RQ0ogpxgSHEZ34GOPx1aDmOMXtHgwpl00VdP4Igqg21Yo4YN24crr/++pZlDz/8cNt6t99+e8v3K664ovl53rx5PqpGEARBjGbm/W+UQ2nLvoeab5sp4pSyHk9T8DWcB9ZUBMqG3on+poEiCIIY5ZABSxAEQRCdTO/E6F+pZHhgW0ScElMBjaws/Ka6y8b6OzaYbU8QBEEECbXmBEEQBEH4ZcI0YMKMSMG2Z7yZiJNuCPGYhr7E6vuivz17aVaaIAiCCBEtDyxjbBKA6wB0AdgO4ALO+W7G2DUAjgLwG875FxrrGi8zgXMOFupE2hZwLhFaIAiCIIiq0DsJ+MQT0ef/vRx44CfAg9cD6/+cLuI0cD+w/IfR5/VPtf6myj6zgPfcBezcBIyfEuUCEwRBEJVHN4T4nQC+wTm/kzH2PQDnMMa6AXRxzk9hjP2QMXY4gGNNl3HO/6x7EL29vdiwYQOmTJnSUUYs5xwbNmxAb6/mPGUEQRAEESrjpwF7dgC/em/0ferLRn6rdQN7TQWe+E30L4bVgPH76e9rZgCiSQRBEIRTtAxYzvl3E1+nAXgBwEUAYvWiOwCcBuAEi2VtBixj7BIAlwDRXKkiM2fOxMDAANatW6dzOJWgt7cXM2daTF1AEARBECFx+seBY84b+T4hYZjWuoCPPgTs2tq6TXcvMG5yMfUjCIIggibTgGWMfR9AYmgUd3HOr2SMnQJgH875EsbYewGsafy+EcCJAMZbLGuDcz4fwHwAmDNnTltMbU9PDw455JCsQyEIgiAIIgRqNWDKS+W/j9kr+kcQBEEQKWQasJzzS8VljLF9AXwbwNsai7YBGNf4PAGRMJTNMoIgCIIgCIIgCIJoQ8tgZIyNAXADgH/hnD/TWHw/otBfADgeQL/lMoIgCIIgCIIgCIJoQ1fE6R8Rhfl+hjH2GQDfA3ATgMWMsQMAnAvgZESTtpkuIwiCIAiCIAiCIIg2mItpWhhj+wA4G8Aizvla22U5+1oH4Jm89QJgEoAtZVeCABBdix4A68uuCFHp52IqOu8eqvL1qCJZ9xBdi3AI+Vp0YjuURcjXoqrY3EN0PcKhzGtRVDt0MOd8WtoPTgxYoh3G2HzO+SVl14OIrgWAEznnc8quy2inys8FY2x5p91DVb4eVSTrHqJrEQ4hX4tObIeyCPlaVBWbe4iuRziUeS1CaIdINMkfvy67AkQTuhbhQNciLOh6hANdi3CgaxEOdC3Cgq5HOIzqa0EeWGJUEMJoEVFt6B4ibKF7iLCF7iHCFrqHCFtCuIfIA0uMFuaXXQGi8tA9RNhC9xBhC91DhC10DxG2lH4PkQeWIAiCIAiCIAiCqATkgSUIgiAIgiAIgiAqARmwBEEQBEEQBEEQRCUgA5YgCIIgCIIgCIKoBGTAEgRBEARBEARBEJWADFiCIAiCIAiCIAiiEpABSxAEQRAEQRAEQVQCMmAJgiAIgiAIgiCISkAGLEEQBEEQBEEQBFEJyIAlCIIgCIIgCIIgKgEZsARBEARBEARBEEQlIAOWIAiCIAiCIAiCqARkwBIEQRAEQRAEQRCVgAxYgiAIgiAIgiAIohKQAUsQBEEQBEEQBEFUAjJgCYIgCIIgCIIgiEpABixBEARBEARBEARRCciAJQiCIAiCIAiCICpBd9kV0GXq1Kl81qxZZVeDIAiCIAiCIAiC8MD999+/nnM+Le23yhmws2bNwvLly8uuBkEQBEEQBEEQBOEBxtgzst8ohJggCIIgCIIgCIKoBE4NWMbYdMbY4px1rmGM3csY+2zWMoIgCIIgCIIgCIJI4iyEmDG2D4AfARifsc55ALo456cwxn7IGDscwLHiMs75n13Vqwxu778d/2/F/8OU3in4/tnfx5iuMQCAletW4oo/XoGh+lDm9hPHTMSrD3w1blt1GwBg0thJOGnGSbjzmTtxzNRj8KXTv9Sy/pZdW/CB330AW3dvTS3vNTNfg0++8pMAgDXb1uBjd38MO4d22h5mLodMOgTfOvNbYIxpbcc5x0fu/ghWbVnlqWb5dNe68a+n/Ctevt/Lpev8Zctf8KlFn8Lg0CBe2PECpo+fDs45aqyGT8z5BFauW4nb+m/DhbMvxDuPfKd2Hfq39OOTiz6JwaFBjO0aiy+f/mXUUce/LP4X7B7ejRP2OwFXnnplc33OOT5814fRv7Xf5JAxtmssvnT6l3DAhAPwvjvfh827NkvX7a5149Ov+jReOeOVbb9dteQqLHl+Sep2Y7rG4EunfwlH7HOEtGzOOT5894fRv6UfE8dMlN7XIj1dPfjiaV/E7H1nK63POcdld12G0w48De+Y/Y7U43ho/UP47tzvYt/efZXKjHl0w6P47D2fxZ7hPZnr9b2kDx+f83Hp7wtXL8Q37v8G6ryutf+Y8T3j8e3XfhvT9kpNIQmam5+6GT946Adeyn7H7HcYPZMu+OWTv8QPHvoBBrYNYNbEWQCAS467BH/z0r+xLnv9zvW47PeXYduebQCi6/+ds76D76/8vvSZ1OGiIy/ChbMvNNp259DOZrtyzNRj8ND6h8A5BwCcOP1E/Nur/w2AvB2Lj2XquKnK+9y+ZzsuvfNSbNm1BVPHTcXVZ1+NsV1jjeqvy61/uRXzH5zfPEYgOoZvnfktTB8/Xausyxddjqc3P42vnfE1HDrpUKt63bPmHnxt2dcwzIdTfz9u2nG46rSrMsv49+X/jgWrF7Qse8+x78FbDnuLVd188OLuF/G+370PW3e1v0ey3mMystrkY6Yegyc2PYF6vY7LT7ocrz7g1VZ1F3li4xP49B8+jd3DuzFtr2m4eu7VGNM1Bht2bsCHfv+h5nNvQ293L776mq/ikEmHtP32jfu/gbufvTtz+0uPvxRvOvRNAIBdw7tw6Z2XYsPODdb18k2yn2zCxxd8HE9uerL5fVz3OHzu5M/hK8u+gstOuAyv2v9VLqpJJHCZAzsM4AIAN2es0wfg+sbnOwCcBuCElGUtBixj7BIAlwDAQQcd5KzCvli2dhlWbVmFVVtWYePgRswYPwMA8PD6h/HU5qfw2pe8tmnUiryw4wX86YU/YWDbAIbqQzh00qFYsW4Fnt36LDbt2oQXdrzQts3qF1fjwfUP4sT9TsR+e+3X8tuKdSuwaGBR88F8evPTeGzjYzh5/5Mxeexkx0c+wlObn8Ldq+9GndfRxbq0th3mw7h79d04bPJhOGzyYZ5qKGdPfQ9+/+zv8dD6hzIN2Cc3PonHNz6OqeOmYsfQDqzeuhpzD56L2/tvxwMvPIA/rPkDntn6DJY8t8Sos/zkpqj8E/Y7AQ+88AAe3/Q4huvDeHLTk5g6biruXt36ItlT34MFAwtw+D6H46WTXqq1r+17tmPxmsV4fOPjYGBYsW4Fjp92PPYfv3/busN8GHc+cydWrluZ+uK/e/Xd6K5149ipx7Ys3zG0A4sGFuGJjU9kGrBDfKilczSuexzOmHlGZv0HhwaxYGABHt3wqLIBu6e+BwsHFmLhwMJUA/a6J64DADy79VltA/aRDY/gz5v+jL6X9KG3qzd1nQdeeAALBxZmGrDL/7oc/Vv78bqDX6e1fwDYMLgBy9YuQ//W/koasPc9fx/+uuOvuddel3ueuwf3PndvaQbskueXYGDbAIDo3l61ZRWWrl3qxIB9ZuszeHjDw83nctnaZejf0i99JnW4Z0103kwN2PjdBkSDf92sG3MPnotHNjzS8rwP1Yfa2rHmvbylX8uAfW7bc1i5biWmjpuK/q392LhzI/af0N6m+WDp80vx3LbncOZLzgQAbBzciKVrl6J/a7+2AfvbVb8FEL1zbA3YP73wJzy95WmcM+uctt8e2/gY7nr2rtwyFqxegMHhQbx8WvR+XDiwEEvXLg3SgF2zbQ0eXPcgTtjvBEzfa+S8573HZNz/1/tT2+R7n78Xt/7l1ub3FS+scG7APrrhUTy5KboHlq1dhk2DmzB9/PTmcz9n+hyt50PkxT0v4p419+DJTU+mGrCLVi/CzqGdOGG/E1K3v3v13Vj6/NKmAbtuxzrc/9f7cezUY3HghAON6+UbsZ9swu+f/T0OnngwjtjnCGzetRlLnl+C2/tvx8p1K/GDh37QNGD37NmDgYEBDA4Ouqp+R9Db24uZM2eip6dHeRtnBiznfCuAPG/beABrGp83AjhRskwsez6A+QAwZ84cLv4eMsnR1/jzladeiUljJ6Wuv3D1Qvzprj+Bc46DJx6Mi4+6GCsWrgBHtG38N20f/3jsP+I1M1/T8tvlCy/HoxsfbVv3Iyd+BMdMPcbiyLK5euXVeGrFU6n1zSPe5pxZ5+DS4y91XbVcXtz9In7/7O9brl0acT2P3PdILF6zGON6xuFrZ3wNv3v2dy3HbXIOktvNO3oeHnjhAXDOW/b54PoHU9d/wyFvwHuOfY/WvlZvXY3FNy5uKefioy7G62e9vm3dPcN7cOczd0rPDwfHSTNOavEOA9FAy6KBRfnnQ/h56rip+NoZX8vcZO32tVjwPwtyr5lYT5frtWzTqMfnT/681Hj8xMJP4ImNT2SWU+d19Hb15h5/GkufX4pla5dpbxcKHBz79u5rdOxZXHDrBcbPpAuS+37LYW/Bjx75kdZ9m1l2o5xLj4vazWVrl4E3/kt7JnV4+6/fbnXexGOM28svLPkC7ui/Y2S9lHbsvufvax6L1j6FNrrI687BMXns5Ob9u3ztcixdu9TuHDqofxwllPZcfWXpV3DjUzcqlfPyaS9vlnHOL89xdg+7Jq7Xu49+N8466Kzm8t3DuzPfY9LywDGmNqbt/L3zf9+JB9c92LKea+Iy5x48N/LuC/3CS467BKcccIpx+U9tegr3rLlHWncOjuOmHSdtk+feMDe17/OO2e/Am1/6ZuN6+ebyRZfj0Q2P5q+YAQfH3IPn4rITLsND6x7CkueXNO2hZNTlwMAA9t57b8yaNUs7OrFT4Zxjw4YNGBgYwCGHtA+cyChaxGkbgHGNzxMa+09bNuoRG5AyO1xENr4bIYaR8uOXbY2NnsdE5VjjdepQD7VVDcs1Dd/No4Za7nNd5/VRda0JP8RGy2iFOopq1FjNW3tXFvE7odZBXctknwAYeUeN5mc8ZJLv+cHBQUyZMoXapASMMUyZMkXbK1303X4/ohBhADgeQL9kWaXJ8kwB2S/T+DcODgY28t10dFOyK7EBdI2L8st6wOO65xkX8TWJ10/+bfG8W3pgk+dB5R4ygrWWHy2S3jxt67atklK/5nnVvJdV7iWTslXXtfEsZF4nll82Bze+1p3wgvTRTonPZ9kwMGcDlM32ofGfuNwGBtYWHaGDWAdZ/dLqanofiG100SSfwZCeR9n50DlPyXVd3sPOaVRLPP8290TW+60IZPeSbR1U7tGsfbRtH5/7kp4/VVy8EzjnI31A1tonFMsOqS0IBZNz4s2AZYwdxRj7grD4JgAXM8a+AeB8AL+RLOsYjI0X4YbPKkfV0LKpjylG+wv0PShD1ji7OtepHljU2u8RV2GItvW23ly/sff5QvD1zNRYvgfWhecsJGNNh2A7xJaUcT1GvQdWcVDSJdKBbIvr7/veqbH290pqPSr0bMZ1ld3/2mHpBQx85pUp9jmay129ByVVVzlXVX3fEPn09/djwYIFbcs/+tGPFl8ZeDBgOed9jb+Pcs4/K/y2FZGQ0xIAZ3LOt6Qtc12n0FAejWLmI6K5+/I8AOSiIQ1h1Fxtg9btXNc7WZ9mOFRGZ9Rk/6n3jsUor055JmXJ1tEJf1Nd19cLmYHl1qHO6x0V+qaLj4GJkLwBySgbp+Uy1ha5EdJxA/ntZYuHz/YclXToopcyFLL6BcptaKKIkD1K0uNxHsRUoAdW2FfcL7D2wKq8a7MiCAO6x3Vwdf+2tWkK0WpVQmbAfvOb3yy+MnCrQqwE53wTRlSHpcuqjCwcykpghsvLyPXiGNbBCUYO2Go87MlwvbTf4nNtfM7bbgHeMtLqI0+ac249wqxzP6r8rpMDq3MOihBxykLluGxCiJNlVBGfbVUoIk6F7K/xTLvopPkSIFJ9R5neE6bpCzZkCeG4LtNVGSp5+YBZP6Qscj2wBiJOLtcz2ndsGAl9DFdRFtJ7V9M7H+o9kYZtXVO3z+i3A5Fo2uMbH7far8jsfWfjUyd9Svr7FVdcgfvuuw87duzAtGnTcN111+FjH/sYVqxYgcmTJ+PHP/4xJk+ejHe961145pln0NPTg1/96lf4r//6L1x77bXYvHkzFixYgBtuuAHTpkUClX19fS2G7WWXXdZS3s0334yVK1di5cqVWLt2La6//nocc4y9iOzoHdovkcwcgpEhm5Y8JpWHKzUvw1OuRG5dHJRfdg6s7vpi/oOP+qi8qEz2r5PTY3ptfd5zPj2wNqImeces5IE17JRUdTQ8iZcc2IC8RUXVpY56IflxWuXltJcu91daNI9LL7JLZA5Ypp4LGKp3WUQWdmuVA+swwsjF/p2FECtsrtR/FZeHdO+n4Or+lXleQzPkTz/9dCxcuBDTp0/HT3/6UwwODmLx4sV429vehq985SvYuDzZxDgAACAASURBVHEjHnzwQSxcuBCf+9znsGXLFnzkIx/BN7/5TcybNw8LFixoGq8it956a1t5ALBs2TLcfvvt+Od//mfccsstTo6jcA/saEA2mmzjHVKZRkdaVsVyYEN52FWn0UlrnOOpK5Lrae9fKD85jU4NtXYPrQPvggsPpk5OtsrvKgZcaDmwKtuo5MC6CCEO5XnSxWe9QzsnrqfRaVnWiNxw4Z2xOm8Zm6q+o7TzFcU2NITrbnUK3UTZyFAVYzLph5RFnvCh6T2Vu56PHNj4WATHRlY0mNF+QtHTKBCfOcuy85DlKfXJK17xCgDAcccdh7Vr1+JVr4rmqD355JNx4403YsqUKZg3bx7OOecczJgxQytE+NFHH20rb/bs2bjwwgvR09ODgw46CH/5y1+cHAd5YEtAR4VYDBXJLDdtVNCxF02V0EfcXJKmQuyjfGDEY+c6D0Xr3jEUVfLpuY33F1oObN51yqvDqFchHg05sD68zEK5LkKIfbVrSjmwtt7jsjywzN0xuCSrbVdtF0NVWBZpvjNdemADUyFW6Rcolavyrs3ywJYU8WeLaw9sW+RkYHb80qVLAQAPPPAAZs+ejSVLlgAAlixZgqOPPhqrV6/GlClTcPvtt+PAAw/Er371KwDAuHHjsGPHDgDyftHRRx/dVh4AjB8/3vlxkAHrASdTqEhUiE0602XmJPjKHywCK08itw8fSbsHkvk8XnJsEl7e3HVlHlhbOXqhXKUc2EZTZpVnbrme7jY11u5FF6nzun0ObCDPkzY+q13iKfGZP5jqGWv856KTZqWgq+hZzcyB1fWWiWUVeN1NNQJMytQqI2P/qu+VSuXA5qTd+FIh9oEYDi32MVwJ/tnkb7f0fyv07vHxXIYaQrxs2TL09fVh8+bNeNOb3oRx48bhtNNOwy9/+Ut88pOfxIwZM/DrX/8ap556Ku666y7MnTsXAHDCCSfgiSeewOmnn45f/OIXqWW/8Y1vbCvPFxRCHCguxFuA8Ee+sqhK3X2PPuvmwIZA0dfOJESwiBzY3LJBKsRZVKUNCJEW9XIHAyGuaSp2FjBNVmjHXjZ5Xm9Xol8h4Dq81nVZRvsXro2rHNiyj6tTEEXjQjPk/+mf/gl9fX3N79/+9rfb1rnhhhvalo0ZMwY333xz23JRmVgsb968ec3PfX19Lfu2gQxY3yTuW5VJ1WVhLlmx9FoNdGPzol5Oth7jMjDNl0p2yOK8s0ZBRqQpDiavtSxP2jaEuFlvmdDHiEJBdr3F7RTPq8mk32WojGah9KwrCKbYdCR1BOBCxFu9WbnnJLnveBod1x7Ylil0uBsPrGp+pLRuORErafvTKiO12PQ2sghsvFg+ydp/WwpTRhltId6BNjMy46753VO9i7jOYr/QVb8uM7oqZxdpEX+hG8bOlP4lxZT9zCe54ooryq6CM0bv0H7gcN76gjB9AKqi5JtaRkVGgItUdI69gaPJA6vigWzmwOZ4NJOU7YFVmbKiDnMVYmJ0Iw5KhXof2ebdudqHD0JVIc7LVfQZdVI0ruZITVK2QdY2D6wkz1e73IDu0SojDtaHZMB2EmG+0SqOLOdU5SYWvSZKHtgcj1laTm5RDXAVc2BVvXmZKsQu8qAzyq+xWnuOrEUYUbLBzbtHbBU9da+vLw+s7Xy3mdsoPGcqHliraXQ8exh84+ulz6A+VYgPkvtmjDmtT5rn31UOrK2nWDkHNqtd1dx/mSrEPjQCnNQ/owidqI22qVwCbWikHljDCJW8CCNxvy4Rj6VoFeK8dkSM0nDtGfaJ1XMpibiSnsdAIsVCwuSckAEbKDIRp6owmvJ3y/DAhv5CcDqHo0JZsZGn85z4FHFSQSUkU4zEGG2Efp+HjBhGHNq51Lmvq5rbVzUV4uZgWbW6G5l40Y0o+VK2Xb/G9bI9xpDu0SrTJrKV6M/39vZiw4YNZMQm4Jxjw4YN6O3t1dqOcmA9IPO+KY1GsfR1m+Wk3PN5o2+lqhBX8CHVzdVMm0YnmQNreg7SFAeTaoM+8rvEPNvMdTU9DKoj3uLvOiGGoXhgY/KmzMoL1bPywBp6GELBV9thm8tpS9v97TkHFjwKo3QShmtRTRcqxNr7bwYnFZ8jX0UVYtVUDHFAxOU97BrZ+8zUK5/3fhP365K2Y2nsQid1xmr/OQNhbVFFwvMXKtb5/ZJ7LO1emTlzJgYGBrBu3Trj/XUivb29mDlzptY2ZMAGSpsKseGzFULuT5llFILnaoqeFHFZp6NiwJl0Rsr2wKpMWcERbu5iEVSmDQiQlnPHw8ub11Ihtr0PSrqNXM5l65K8HNgqDjzL8BHGWva1lIUrkwc2LNJyYHt6enDIIYeUVaWOIqw3WocgG03WUiEWR45THoS2ciUNdFp9QjaAyh7JVW3ExVG3NhXijGtmQp531CYPpuU+i++9rJHWjLy9vBwhVc+2WLcsTMRHfIo4qXQAVXIfncwDG6hnJA9vodsle4uS+45zYJ1VJ8XjwcHdeGiYP++hqk6DcQ5sCdEIPj1wVmVkXIemBzanzUuLkgnV6M0TOLJVto5pMyoLuP7xd9dCVdLoKpjlwIZuF7vK75f2wQN9NqoOGbCB4iM8lPADzQPrl47NgVV4aY72eWAJN4SsQlwEIQ/YhkQntzUdff83XiMdfYwVJEt8lbCH7nYP2OTAivOwNb8bzgMr5iS4VquTYTPiXbaXWFmFOCsHNuEtNW280vKgk9PolKVCHK+vO0qrq+4sbpeF+JyoUIgKcY4XO9fTYWF4mJyT0PDRTpU+Z2Vi3649g7L3Q1tKigGuvWyiZkBM1rNjmq9YSj44F0ScHDyPheXAKnhgxfDoUDvpWR5Yk3rL8kBlwkoukd3Pzetl21zGAYBZXvqcfkHyuFWiDkPAum0TIl9oGp1iIAM2UKquQjyaKFKF2Ec+T+joHGulPLAK940Lw4MY3VSlE+mT0XzsOlRd+C2NsgfEfSALV+5kD3oVaRqwFR5EDhkScfJAag5AApUcWFnHVXdkTDq67dvoshhtLv3l2UxDVqtHM/dV8MQ2j93wcNLmfGuGEKeoEDfrY5MDy7nSCz8zB1Y2Qq3YgRCPS/WlXGO14HJgs66FSn1tQoir3hn1OfVLMDmwjSgbnyrErga9XCl1JstL/m2ul/LsmL5P2o69UAdsu5cyWSflchx3frPyGJU9sCkqxKGSlR+qMhe3SF6EUXI917TldPNWD6yr6aak0VV5KsQl9TdtcZ0D24liaCFCwzUEEThpHthRleui+O7TDQMq2wMLlv+Cq8N8Gh2CAPJFbAgipiPbmg7MD5UZkeSBDRMyZP3g9G5njF3DGLuXMfZZye/vZ4wtaPxbwRj7PmOsmzH2bGL5sS7rVDqa+QDiiK3KqF6Wx0zMSWjG6hc0YmqUA1vyqJ1urmaznnF1BbVO2xzY5nfOW3JdfOTApu03df2Ma5OnVKiaWxyj+lLWHkVVXNUqBzbLA5vhRU/u2/RZrbrBMppUiF11cJLvGFGZ23qKDVsvhXiMcbMp8YK1ePhMvZeix6rA6962L4X8wrxyXKkQ57UpKu8+lTkvQyBLgdu0jUzdTkyBLeB8tOVYWjb5eZEOKvefru5LCNjqIsiiSygH1i/ODFjG2HkAujjnpwA4lDF2uLgO5/x7nPM+znkfgMUA/hPAcQB+Hi/nnD/kqk4EUQTeVYgF47KTRpJVUD2/uoaAamiwV0NKZRqdihuiRLlQDmz4HehQiN8tTqZdCoROVO4Xn2VXg1SEW0iF2C8u7/Y+ANc3Pt8B4DTZioyxAwFM55wvB3AygDcxxpY2PLhtebmMsUsYY8sZY8vXrVvnsMp+yFMhzupHiKPOKi/eTFXBsnJgLUa8yxZdUK27dxXilI4n57zpXWnz0DoanVe6R1jG/rgkGsDwnlC9D2qsptXxUg4hthhJ18kXku17tKoQ+8qBLXvOyuS+Zc+ycdmJZ7fpgYXD/DgPXoq2d1SaN9bwfdWm2lrgdW/LEzX1IqfMJW+LNAdW9TwJbXzZUQ1ZZObAGjx7qirEXnJgU3QxWpZb9uvyts+Lrmqb9aJCg2cuokukKsQVfQeHjksDdjyANY3PGwFMz1j3gwC+1/i8DMBczvlJAHoAvEFcmXM+n3M+h3M+Z9q0aQ6rTBD2FDoPbM4LpBNRPV7dDrZPEScVGMufRqeOOnmPCCsoB5Y8sKpUXfgtlYLTpopAZiyP5mc8RMhw9YtLA3YbgHGNzxNkZTPGagDOBLCgsehBzvnzjc/LAbSFHleNLAU3QKORYXoNklbj5bmdc/GyKC0Hlqm9xGUvDXE02rYRS3rS6rzezJuTrm9w3tI6LqYqxLI6qJ5XEVUPpIpBWBSqKsR556LOzUWcOqEz4+MYQvMWefEyM9b2vLkIL3SqQhwft5DPn9auulJXLZo0FeIQyFMhVnlniccWamc9U6HX4SUp8vpKlbsLeE7y+gWm5ZaJNxViyoH1iksD9n6MhA0fD6Bfst7pAO7jI63dTxhjxzPGugC8FcBKh3UqHR+Nuu4csS6NKV1CfakVgY9wr6QH1kcIMWBfb9t6iPvX8cDq7Lt0D6xCuB7n3FpZsqovz6rWO4+ij6tKYXx5lN02udiXqRCVyba6+1eeRqdCz2aed9JGVMvFeib7FqeFygqTttlP23KFc1Wle6MoRnM/2CcuDdibAFzMGPsGgPMBPMIY+0LKeq8HsCjx/UoAPwGwAsC9nPPfOaxTKeS9uGxUiLPQmXuzqBxYqzJKzoHNa4dlCnviaLS1CjFLfOeRJyVzFNRChbjFA5tzn2rnsqrmV0nqloeKRzOJz46IkpKzgkc69rgbUX17xY8HNiBvkes85SwVYmvvjK3nWthUmgOb0q4a58BK2uiicOlFdoqkKvFgmUnbHqrhkhWB4FKFuMzr60qoSuUYdKK/XKkj+8b2ndA2SMhal4f6bFSdNsEkUzjnWxljfQDOBvBVzvlapHhTOeefFr4/jEiJuCPxNQonCvtkru/AmCqSUDqXqkhHdh2d62T5LQaNUHwo583aAyuGGup4YHXmgVVc17cHNitM2MU8sKHcF7pUoa0yoejj6qQcWNNzF1Rup2YVfEyjI0N1QCWI86hIXgSCjUdchq9BslwRJ0dGtM00OsQIJOLkF2cGLABwzjdhRIl41JL38Kt6zxiY1siVTGVPdV2XBDXarIlyDqzkxaiiqqlCm4Jm4788z7xVDqygkCpdP8cb41SFWPV4mF7ZPl/GSs+6wvkYzfPAAp7yQwM6LwzMaU5umsfDlRfS2kshGZiStZct18mw6mWHT7vwIvsgV4VYI4Ik/hyqcZN1/xt7YHPKKlLYEVCL7nO2b43or7KfP1VcRRAk2zSAcmB9Q5NGecanlLrqPlyP4Opg0uGp2sMua9Cd5cAmyk96YL3lwNp6UB3nqal6IGuspuUtLTsHNj6urPNV5/VRmwNb1WrnUvBxddIckaZtSxlTSjnzYiVWd1F/JzmwFfIo5UUg2ExrJMOXwSYaqnFdXKUJiPtJ+SF/2wrdG76hc+GX6r/RAiTv4VfOgc1Rm23bVmOEsQoe0rJH7YzD1Ry/RMSXVZ5B40qFOKuYvClrXOYIqW6nHUKseH2NcmAVRp5Vc2BJhdh5ocEY9XEb7zMH1uVAmpVSpyjOFit2sta2JFWF2PA+COU6h4asTW0OqimGybZ8DvRUZ+WHOo1uYTm/O6TN0wm1vkFuuQr11jm2Ij3DNtj22fJUiAk/kAHrGS95EOJDobGLwlWITTr/HfLQ+ziOOq+PCAR4Ok9lK32aqhBrizgpHqevZ0ZJhRjmIcTJMqpIVeudR1k5sB3hga3QPeFDhdgFStOf5eyyitdB+h7xdCg+z5HMQPLtgVU5pirdG95pnAryxPqh+m+0EJE6YD3l/mWVEYD6onEZJXuJVcWx0o7VqQpx4jsHz1UhLorMULSM6+9LhVi37FBUiLPC9VyEEBPthNah8BH+3xQJczjFhssc2BiV/DNjD2xIObABtNkxeZFZ8T2jQ6iGi2uBI0Dhfir4UrtUGndJqPdEGi5UiGPaRLYqdB6qBPWMSkAnCT5tXVnuo848Z0W90G3CL8vENnTbJW05sBLPnc2LOrmNStiPz2Nuy4FVbKZqrKbV8SrCA5t1nlSOyyqEWKJWXRV8vfTLDmcThdJ8TRUk7s96io2Cz5vKu0+3rCI7ktIcWIt5R73nwGqEnZf9HKmSNYBjIt6TNRDTUm4ZKsSOrolp/jZj6ccd0uBNkZAB6xcyYD3gKnSoIu+HVFw0WGW/IFXFsVI9sA46HbKXUo3VvJ0bV9PQZF1/X8+BbqehbBEnFQ+sjQpxJ+Dr2EPrUHjxwCoK8hSFTIVY/M1lB7jsHDwXebw+kNUlHuToRA+s0xD6XAes50FtofxmDmxog1Rh3hKpWOX3S9q25jywAThlOhEyYD1jGzac2qC0pcCqGVrJz4V5YCuqQqzTYWrzmrsWT0lcq2Rn1JsKsWXIley4TctTfSnrisz4DCGOURFsy5xGpxEybrPvEJ4nE3znHpeFr7w1WZmucmDLPm8AjDvEpagQOxrILnIed+UcWGFgLeRBtqz3mcxjqFJeW1loPR9eZqBA67GIBpLvZzRvQFXm0Q6i7cjAVf1kAwtVfQeHDhmwHpC+uHRz/1zkkQYw8lxmGTaoTuae64G1bLySLyuOxjywAXimCsuBVbwPtFWIPYYQK4XgKebAlv0clInv6SiCwVV1EuWIAxhOcmBtvBQZKsR5Rppt3UN4D4b0HEvfH43FJl77UL1MPp71vGtZtAfWxGOeWu5ozoH14IFNrEB4gAxYz5gqqmat3+Z5y/GYpYZnFfQurVIDlsTmBeQ630rMZ5N5UgrNgc0yUCXHrXpOxWfG1zywZXtgVY6LVIjdU7a3SAzz9Z0D60yFuODT5jIH1tX2LvZlk3Ppov5Zhmacl68T1QWEZZyL5N3/znJgBY90ITNQxMsdh0ln5cDm9QtSB6AC9tAD7urXNr1R7CGv6Ds4dMiA9YCrCcyrrORb5bqrkmUwOlEhFgYbYhViX8IvLftUWTfruDKq51OcR6dsnzmwruphI+LUCXjrGAfWn/CRAxvjMgfWZac8vrbic+tyHz4UaHWoSphtjE3edKiddB9pU2VfS9k8sGXXSyRUr3waLlWIk302IBwdgk5j9PaMCkI338m3CnHZohYqhNDoaakQC+u6zj0UPbAt3lJH11am0ChdP0ukSSFHKAvT0X3dl7fq1B1W1zGjSirl20yjU0ben0s6VYU4LczX6+4cGXGFqxArRB/pllWokSXblWYVWtp4zyrEqm2N6Ikr/ZlSIG0g0EiFWCUH1qBcnX2L93PTA+uoS2+aHsQYC25wUAVvObDkgfUKGbAecJYDW2El39BGAk2wUSEWVrTaf1IUqs7r3sIOk/tUWjdLhdhh/ZRFnMD0QohzhC9sDECVbZqKn6RCLCWEXO8i8KpCDEchxLCrp2wwV+ZNSlvXdJ+UA9uKtC42ObCBPVMxPrxfuTmwvqfWE/bfPEbL3bq+R0O9J9JwWdeqCyhWBTJgPaPrTVJpQHQ9XGnhWUV1iquqQqzTjntXIRbyU5OjrK4ET0Qvr7hfnX2o5AhlIRN7ycNUhVhWvouXkGkecUwdFvPAVv0l6qvapTtgE0Ym/AiypUVpVE0IKdUDa9imljEnsqscWFfbqpShnANbQRXiVA+siQqx7P1WwGCFVIUYxXhgO1aF2FcObFXfvRWBDNiAGQ15pFmUXXfVF5svFeI2Dyz4yAsk8MtahgqxtohTngfWYiBC5ZqreHgpB7b8XO8icFWfZDltKsQO2lOnKsRgLX99RHSU7YFtIYAqxEgH7WwECMN6pJp4yYF14IhwuX9Xz/hoViG2qap00J2n/064YfT2jIqiLbdbr+FzoUKc9mAW9UI3acBCeNi1zo+4arPt8pADK+QepeVHuVIhVq2TiLUKsSTUUKlOGqc8t2MTh9NZTFGgkkecG0Js+KxW3QPbqTmwZakQW3tgCx5QdKpC7LhNVsFVKpHrOmeqEDcGy1SmkAs1PFok0wPrst6s9bPXfoxwP7ueB9ZGiLSSKsSePeZVfQeHDhmwHghKhbikF0uV83djVMKogPxQN9MXWdqUR95zYB2pEGeKPGk+B6phUYwxLWMzb0opNtJL0EblGFXC9Ua7B9ZXExBah8JLDixaB0ic3Ec2XoqcgS2xM562jvY+HXfsdWkJsw3IyMurS2VTf1JwNUdqklwxzqp6YB3XOwRnhCpO8/vFNi3QZ6PqjOKeUTG03bg57YOSCrHQKOSqEKeEsxbmga3oi9CFCrGrwxA9sMmOqKtrK+4jt5ysXTi+fDoeWJ37TdUzZeWBzcoXUgghFq+30b7Lf5yM8NX5KduQEMN8fXtgOyoH1vBmdq1LoIKrgWzXKsRZNIXl8to8LrRt4djmbeR5YL2oEGvqMejuWzSQXA90ZuVv50UVuRyAqhpSFeIKGfJVwqkByxi7hjF2L2Pss5LfuxljzzLGFjT+HauyXdVwFTpU5We+yt7jGGUV4pxQN+sc2IShE4eUVlmF2FckQo3V9OqvaPAbvXwUNlEJ8a3zeunPQZmEcJ8XQREqxMHlwMb1Ya2/e1EhDmA+9JCeYy95/4F20su4B7x7YEWxIItUk5ZyA7pHi4Y8sNXDmQHLGDsPQBfn/BQAhzLGDk9Z7TgAP+ec9zX+PaS4XWWRCVfIUMmBle5DmgLrJk/ShKo+uDrnp+2a2Qhh5JTPIcwD68BQFqmqCnHatirryspPjm6bYpsDazOyTjmw6ZSdj9VyXMxPfZJlusqBDaJva5wCW/yz4EqF2HUbr5L6oTJ4G6pxLpI5R6pBrqqqCrEXD6w46MpHlhfRrpEKsV45fOQCER5w6YHtA3B94/MdAE5LWedkAG9ijC1teF27VbZjjF3CGFvOGFu+bt06h1X2Q1A5sAGMPBuXUXbif87lygz95O46HcnOV+yRC2F+TGPPsmaHQdWA01YhVvTAmqCUA8vyc2A5Nw8h7gRCyPUuBFfVabGNW71pTuaBtThvMi+FirfC+D4o+TKLYaXBkJP3X9XUnzRczZGaJNcRUfA8sM48sKNYhdiqbctTIa7QeagSLntG4wGsaXzeCGB6yjrLAMzlnJ8EoAfAG1S245zP55zP4ZzPmTZtmsMq+0f20pYi/KyixJibA+vQmCqCEDqXWjmwsnlgXXlgJeU3djLy0bEKsYr3MA3nKsSK2+mOejc9Uznq3VYeWAUvduY0OqibdyqE0Myq0bEqxAXnwDpTIS74vLlUIXYdFaOCDxViF89yZuqHQlSIWCeg/Gcqi6z3mVEOrGKEkY92V4y2a0YJwW2qSZYTJrNfIDuf4d4eANzdvzJxrSr0u6tIt8OytgEY1/g8AenG8YOc812Nz8sBHK64XaVwFTpU5ZyGUaFCnPFidNHpSBPrCkqFWKEjlLqdx9BQVyrKJuuZbpOrQlz9JtGYoLxWHvGRAxs3EU1BHgen0uWzK/XApj3Dxg5Yf1EWKlRNhVhFGV1GqJ10lxEIMaGpEIO7PT5XVGnw1GUObHM5iTh5xeUdfz9Gwn+PB9Cfss5PGGPHM8a6ALwVwErF7ToG3YbPRg3X1bqjEa0c2CwPqYu6JMrLy0Ex3X/q6LRhDqx0G0NjRPXFrDuarqrO6uvl01T8zJsH1vC80TOeTkjnhYH58U4kgzSycgC1iizYA+twf6NlIMQWFWV0cV3xc2hkemAd1rvIcHHxWOKB7SJQUdZvWx5Qm5uGLw9sTKiDO1XHpQf2JgCLGWMHADgXwDsYY1/gnCeVha8E8DNEr9dbOOe/Y4xNFLY72WGdSkEquqB5D0vFZSTT6KSWIXTqi3qQOiGXRrXuKqHbRvuPX7yJTkUdkUcuLSTOmcCHYjFZo442LwTjEGLGzHJgc0ScjLwRKh1AtF/DtDrYdkxCeZ604R5zYAM7J64GScTw5OSyUFWIVfLFTN8novFSqCdEuH9N72XXKUBZbYpq+kvqeQzrkWqSlyqifU8pvN98t1viQENeaK8quaH2Kqeqpcsb6E3hmaoLKFYFZwYs53wrY6wPwNkAvso5X4vIw5pc52FESsRZ221xVacQ8dnw2XgNCXNkKsReyucKXnzLHNjU/Wr85nIbQEPECZrT6Hj2wOaV2zyujOJtQohDH/UujYBOi690gJYc2EYIsW2IYdHvDF/eMUKOTq5wZVSIM4w7X17+okOIi1IhTtu3ym+h9zd9qxBTCLEfXHpgwTnfhBFFYe/bVQGbG1c5HCFjF0UIC2Rhk0sTeqPXRFJNHyJO4svYxsMvw7beti/TtntUsSimOXl8c37MnOesmUPomjhHMSeE2NbwqOoocFXrnUcVomCKRKWzb+3RKOFVwiMXbFsdyr6vszyIKmkNQPnHoENeG2oi4qTyfvM6jY4w0MA5d6KVkNdmqBxTle4NX5AKcTGEl/XdCUijLxTyF8V8Sov5SF2tOxpRyafM8uC5CPvKEnHKogjvqJGX17BDrfpi1hZxypk/uW09DVSedRXBFBsV4soMABUMg9594pNYhdi1iFOaCrG1B9byvKmK4qH5WDoIeVaMsiAimudJ4TLLFFdDoygPrFBwobQNlpSFUIeqPH/WbVuOwR/K+6bTIAM2YPJy82TfW8ooqbNmM21BVRq9mCLDhTiikdY0Y9CLkqkhTnNgFQ0x3Xtddfopn6rJQLa3o87r9h7Yir48iwyLK5Kirod4f4XWnrblwCqsazyNjq0H1wDR02mcx5ts411Mo5Nl0MX3jK+okxKwEcJLL1AtjNbLcy4M7jSjhBy8J9LKbd+9gge2ou8bl7Qpqwc6uFN1yID1gM38bzYqxDp0YsfQJSrhqLJQ5zbhLNNpdFLKd/4ybpD20s3bj/ZxGYbQqT4DNVbT6nipitsYeWAVhD7yPMu26rGhGSyh4NLjaQsD/8NdJgAAIABJREFU0w59zyK1zZC0U7rY3k+q7anLa1O5dJSS0TG+RBXiUJ4pkaw5Ur2pEPse1E5JDSuqvdeZnaDsaayUYXbtjqyd8T0IPtohA9YzXvIgNFSIxQezsNF/i1HIqnU6fKkQp5bZ8Er5UiEG7OrtxHsupsBqeGB1TkGeZyo5uu2FnBzYPPXMTqfITlmRFJ0DG6wHVqM+tl6tMvKBRU+nqRfZRxsva1NiL56RCnGo8OzweVNlaxm20QIq+xbvZw57rQTAzWAuGWsp9wCdEi+QAesBm/ALIhzyGussQ9tlpyP5sqpz+WiyC7RUfA2PS7fDoHO8JqFvuSrEvkKIc8K1XKnHEu0E1xY7qk7as+VyIMTlNDqy331cm9E6CKSL1cBzoEZtkXOklkWo575KeMmBJQ+sV6hn5Jm2ue80R51V1s8KhRTzAgsb/bcZhRTyPMrAxXRGzkbLxRxYJsmBtZnvMbGJSthPrkJ2ys+q51Q8b8rT6LCa1ktItWPvexqdvJefqQFb9bnofOXAlt2ZbbkezE87l3x3xPuzVil1XM24firhna7OUaE5sML9a5wD69gDm7V/lbz8uB6dMI1O/Lt2eWl9LTZyP+sKCursO95Hkjoc58BmiBLl9QvSzmfZbW4eru5f5ZlDCCeQAesBGwlyojoUpUKcNIh9h1WaiCBp70NzO9XjZYxphfuq1sNIiExhm1wPbKChn51AaB4Ln+8Gpx7YAlSIXV6bqgkClo3NoFeo/ZvR4oEN8R4PrZ3NwkUOrMsyiXzIgPWMrqKqKIyQGp4qyYFNa8DaBIUUvGsu0JHjFwnhodfylKeIOAHF5MCo/paH7N6Rri9TyM7oMJp6YFU7H7oepsxz6SJqIafazYEJyUveevqT+BGsUCciia9OWekdvRYHrJ+6sMi1C8DdQIjruuapELfsr/k6MYyGyHnWvMDdCPuUoUKslAObvDyBG4hZ0+hoe2Al7VIyosCXUFxz3ynPg8vnM29mC+lvTE+HIhRce2DbcpQr+g4OHTJgPWCjQkyEg7IKcYYxCZhfdzHHNs6B9ZkTqZUDa3lcqii/XJie4JJvFeI8miI7krzdZugn5cB2PK5ViFuW2aQWKJSvsXH2zx7yxSqjghoIlANbTUI9xhCcEar4dDhU6TxUCeoZeUY3B1Ycsc0zjpLfU0cFWfqULkU1djahSKU2yBq7bpv6yPFof0sOLM9XIbb1wKrcI3m5HrJ7UQXxvCnnwKJmZIDnDkCYen1yrkNeDqyt56zqObCAp/zQgHJg43w557D2ARLbgRDnHljBW5G1P2cqxAU+C7JIkrJViLNyy5U9sOiMHFiTXFVZec1lzN/5aM5CIHr4Mq6pDnnXP28aP2kObMD3B+DunSBGlVT53VsFyID1AN20nYHqiy218XNwC7RJ5jf+sxZjUdinVzR3oTyNjmZnJO7Y571cTabRcZkDSx5Y94TmLXJVn0wVYgedSB95Yi730VZWwQO2VSd+t3ibOqwEfEcthUCd1732C0ypVF/YJrhE1n5X6PCrSHh3fIfRNgqr6YFNW72tUWh8lakQJ1cvLAfWYgQqBOENKxVix6P9yesq5h61NJwZ94HWPhTukVwPrOxeVED3mWmux/TyjprnLu0Zc5B3lvus53jqbTvfpeT9OYSDp16bqpO8HklvikuSnl1XRpwvI1BJhbiCHg2ZV6zs5zErt7wTPbCAmpdfq7y0Z6HpgPWoQty4dmnXyYkHNifSIU+FWKyTy7r5xFsObIXaqypCBqwPpIMxdDNXCSUhC4VtjXNFBUO+CA+szi1amAqxqgcWep0G1cEcbyrEOVNWqHqICX1Ca4t95sA69cAWoELs8tKEdp1Dx2bQK9RznRf22gn4nh/elLIHa3TwokJcoeOvImTAFk1OGyNTtE2io0Kc/D2vXB9U9UWo88KTnksPhyG+jH3mwGYWI/st45iVc2CFQlQN9hqrSQWRUveT4ZlyMQCRd7zxcfkKIQ6xQ6ODr5d/2eeliBzYtDYitBxYk/1Z58AW2KF0NZ2eDxViGcrniQvtW8BNTR1y485UhVhWVst6BfRjXOfANsvNMsgydiPzPJfd5pZFCH3ZToYMWA84G42p8DPvosEqu9HLu15Z4bIuhDfayucjLyqfIhHK62acn6z6heKBVfVM6RjFTRSqoert6PT8rSxCuM+LwEcObFPEKZD5hNsEDUXBkwwVYuMpaDLa6CIINcxWdj7itsakzQvV28Q5L7QN9Z6ehfb3v6s8X9fPSWjtbBYuPbAUQlwMo7dnVBA2KsSy9dtUiHM8ZlVVIS4TnZeQVIXY0XEkyxdDhVI9hQaX1nUOrBWGReiOpmet68QDq3gh8jywxjmwFX+J+qp32eGEYt6arxzYmGaIuuVuCj9vDncXggpx3nKVcny3r6oe2CrlwGbmbTL99l12DVpytg08uyb7TobdF3EN8nJgpSrEgYdwu1Yhjgl1UKdTIAPWA65Ch5x4MQMYeTYuI6COZurvWSGo3F2nI2kQc0Sjyb7OjSsDMKt+uo266siyrnCGqliYrzD4prdDlgMbhxCP4mbamwd2FHQsRGPExX3kMgdWFnLpNAQxHtMr6z2YIkQVArYiTmmEOlDmIwc2cyoZ39eZtV+/OtzMA+u8vQ3zlkjFqm2TRJcQfnHaM2KMXcMYu5cx9lnJ75MYY79ljN3BGLuRMTaGMdbNGHuWMbag8e9Yl3Uqm7aXdt6NLfys8iDk5sA6NKZ0MdpfAI1eUCrESY+roCCZdm1tX0JKHljJfZl3zCp1M1YhBtPLgc0KL+Tt6+mS9+zm3SdNw8M0B7YTVIg9ULa3SDcqx4S0KI2qdapUoo90KdQDm6MublJOKDmwpm10GdS53LjzlQPrU4VYttzlNTBVIa5qDqxrFeKYUAd1OgVnBixj7DwAXZzzUwAcyhg7PGW1dwL4Buf8dQDWAjgHwHEAfs4572v8e8hVncrCVQ5skCNqqvutcN1VyTL0XKoQJ8v0ngNr4MHU3oduJILivVRjNSMVYlfrtWyjUI+mYSpZtalCXDHDwyWj5dh9qBCLKtdOVIgddshkU064DEF0NahnSlXCbGPiNqmjPLBwr9xvkl7jct9toapwk+fruu6h3hNpkApx9XD5VPcBuL7x+Q4Ap4krcM6/yzm/s/F1GoAXAJwM4E2MsaUND263uB1j7BLG2HLG2PJ169Y5rLJ/dEcqlXJgLVSIC5/Y3cgBW/5Dr6VCLFGOdtV4ZeXAJrHtrDW3Uwi7U/H2p26nElFg6KHSHfXO6tgn70FZiK8rZF5jWxXimBCeJyM8VTskQ4JF8YDuy008Z64GQiqtQlzhaATXObBOPLBCWG7IA01ZIcQmnlJpDqwwWFFIDmwsfOb4vpbWnZv1C0LHWw5sVd+9FcGlATsewJrG540ApstWZIydAmAfzvkSAMsAzOWcnwSgB8AbxPU55/M553M453OmTZvmsMp+cCXeUOU8UicNWcltobIKsS9vaMrLKg7hCT4HNuOceMuBNew0+DiXOjmweaGGVe0UuIBUiO3LcZoDa+OlsFAhNt5nSB7Y/9/euQfbVdV3/Ps7995zuXkQQhJCSAghE0AJD3lIUFATHjKWilKtOgNOYVrptGV8TFtbwHetUnQcW4eByqCMjhXwEdRqR5sgFQmgQRMMShCBJIY8SO7N697kPn/9Y59zss8++73XPnvtc76ff+65e6+99m+v9dvr9Vu/37Zokhc2oQPK6/rjRx7vehr3mjzvXd+ZZSBzo9jWzuaG5zFNB/Ik/rRYOzNwCMBA7fcMBEyOReR4AF8C8I7aoadVdbT2ez0Av63HHUNSC2yivE18u5QkJigKscEbNK2MR03oUketjfEN4jjnQu+R4rokFtgk1tLYn9FJaYGN+65HRSHu1u/A5oZFxZKnO0CdrNGs67R7AmbyfnwX4lEvpzhtXlm2R4d9Ysak3EVapKd0qm3B/tKMC2xavPEjLx9Yki8mNf4pHN02fC6Al7wJRKQK4FsAblHVLbXDXxeRc0WkB8DbAWw0KFMxuAPAZFhZj/upkiTf42zbilBjN2p6/8GiG4NI2UO22tatpbHyCcrezwfWsx3KeIAPl9yprq9bPDJ0WIkDn9VI2oGHRpH22XafKG8DFtj61s9u3UKs0Nwmm0VuJfWrj1x8YD3bQU0MIvOIQtzSR4UEgUkbcKcIi0iQ/posw1R5hAT8iVtOeeqwaaICDyXWqbjWzhyKwxsDwz3GMBl3JMuXNEzE/ygKU3E9TLuREX9MTmAfAvBeEfkCgHcBeEZEPu1J85cAzgdwWy3i8LsBfArA1wFsAPC4qq4xKJN1REYm9VrBcrKqFj05tB2byse9ZXgKwT6w7vRp7xM7bcZvkya6Ju69JJm1tOEbGGWBTRDZuEmcmBHHg2TOOvGwfdW7KKx7t3OQp8lvPqaeJ8mzHdhUT91C1KKam6b2xeKqCtu1lJeOtVt3TUchTk2ACFbIFoLt8hF/jG0hVtUDIrISwJUA7lDVnfBYU1X1LgB3+Vx+jik5bKC+4lePGts4njEKcSPPBPm4V4LaOaDNsgJVtN+SV46o83lY8Pzur3rUApvWIpHmvoHpQvztsqx4e8/HtaxWpJKsPEKSNj1bGnewOFGIER7x09R3YMu6CpznoKxIa5Hve5OHBdYThdhElNIsuNsGt3UyToCltBbUlja6jdXu1d+sVuTaxdnlirBIttwzbr6WtjPG33WNt402lzZGmxe0c7PABsgeywJb4GcbsxLn3Qi6zg0tsO3BpA8sVHUIRyMRdzVpvwMWFoU4Ks92WNC6hSyWb9Nl67XSRE1oTFlgc9GRFFnGlUOQMApxzOisqS2wUZbyiIE7fWDzwaa2L6+AbH5uBrZYYOP2jUb9E/kuxKLe1sRp88riAxtqge0QP+u2+sCmiEJss34AMLaDgFGI20uxS7IdStMqjjYfz0LQ6lhovh5f1HatCGVZhTThR2mCLFGI69ZSd7rE9/e5bkqnAPG3SJj6xEJcHckrCnHqz+ggWRTisE8QtMOPJ+5qt/Wdf06kXQ2PlbdlPrCmxjnu5/IG5DFRlkb84xHg+4rg9jKtRcN7z3b7wPpFIU5rRU5zbRCBUYhjlHMW/8giiPSBNfQZnTp5+lu3+MC6xhhGLLARsscpK1v1IA6pd8sFRFgvc1mUAU5gcyLtSlSoBTZqAJKg/erWQXFcspSP6bL1bhmOtMilvb14/02+0hp9i/x8YNN+BzYyXc4W2CA5skaPLXoByFZsa/va5gObVR9MWSniZmSwWPguxKPhAxtj4F0mC2w76r/IKMTtdBFjFOL4+XALcb5wApsDqkf9e7Ksnra8VAHvWJxtxXl8Yy+MLCtQtkQhjiIqim1mq7d6/1VMqRPEyW+lvN1RiMMiUYZ1WJnfg5B0aSebXjL7wCYYAEatdmeOQlzSTtSUVSEo76JoVwRXr9+nNVGIpf6n2RIb1l6mlr1xy4IssNI6ycti8TMVhThLmiz+kUUwpVNG+6OodilP/8egqNqm4gX47Rxsun+Msip1FOKUOhzkA0vyhRPYnEi9EuWds/p0gEnvSZKTZNKU9tpEsriyjPwOrCHraOhKaxujEMedwFWkkqjDzDJRj0XEo0ZZO2wJvtNpWNdO5iCO+/00FQwsLytF3vcj8UligXVXj80WNoUG6n5eftZtj0IcZ2eWIUIn7xFb020lNwuspYs6nQJHRjkQtGJqIgqxXz5xfBHD/IvywMQqZNGNXuwoxD5yun1JTa3qqWqLP1suPrBxJ3YhUYij7pHlfBBptxBH3S/Jp3nqJAlSE/Ud2KzvQVk7UWs+DWGYtO9N2ryNbSFGRl3yXFqXxxvMLOweqX1gY0Q6No76T2qyRCE2sssmxILo9ZuOkifO8aKJ2l6bpj6KikJc9+f1WkqNWWBN+G42bV6yUyeCMBmvBLD3negUOIHNiVz8mixe5STtoZ0rrbYQ93kTB3HK2wIbQdSg2tQWYtLd2OaSUYQctjy77XRiW5NnMDhbyNPdgqSnbBP4stF5rZUFuFf8kkZU9a7YxlnBDfPF9POVCEprkiwdhtdXqhAkevUsaGVXRADN3ni5rQf1yVnDBzbk27NZthCrI3jjvpFpE8pQL5swUkchFklkLc0zCnGcQVPD2hHgtxvHnzhO/mXuRPNaCLRpZTzpwksY7nxaohBnbPOzlptXnwN9YH3akKzWyyKigqpjgm2Qtvzz8IGNeq9CLbAh9WMjYT6waXQ6qG1v3EPyKw/vRLVeF/Vxgcn7tByLsRDmbcsaZWuvegBojReQGM+YqZAdH10IJ7A5QQssyQXtPj2I7QOLSjILrIHPBWWh4W+W03dgCQGOLpDYoke0wNpLJ37+gxZYUhSd9B7ZiB09WgcS9J3OuJ/WaPz2SR70UoQGFKr7SmS00sWmcdsU1isLtrzFtYr4WUPr12aNQuz9pqNCMYXmlVa/aLmZPrui8XQkyJIaZTWMU64t52M+TmIfWAT7wLrzSesDG1UPUValrMF3yr4K7LVgGc+7IPx2GJi2wLrbJVP1n1XOoLahpZ3xacdMfEO13QRZOhPXh7uJN+UDG/Bi1Rc50lhgbR2sqwa73aSRO2iy6N5RkNcuj7pO+e2syxqkrY7AX/ZYY0fPzrW2jTczknW3kvc5jbz3JBJOYEuE7Y0AyZ92fu/NFuJ2zIl9YONaYHPqeOL6wHZbfROz0JLPdygunTjG6BYLbIc/Yinxm8wTc3Rvj5YjQQ2m+/uwQYT6wAYMeMNWuYJWrtvlA5vWf9CdRxHEWUFtiQjoOee1eifFrUdu62gFlUALP5DdBzaO30qgD2yE9TxuuXqviUNiH9gQC3lWv7Mkuy2CZM669bPsPrB5RSEu2lrkp9+moxDX/eYBtEQuT0vmaNietiEqCrGvD2zaKMQZ+qO0eNsAW6zIoVGII9qkWgZNaZ1/7LUwRfnAJi5ejbE7Cfm1u+4xR73Mp3TKnAVWwvv2JOMCG8ZycchaZ96y8csvzU4uEg4nsDmRxwTR9kaA5E83WmBjB3EqmwU24rloOSMmsG0QWUT7Zcuz204RE/286RYLbLeNC8qA6QBspBmOjHLAbTnwbiFIEoXY+3+aKMTuezelzblBz2L9yRp91QSxfDVrFvUWn66aRcUbVTMp7smq26/WbwXW/TuLD6xb7igdCXuuTN/d87rAxnyeilQSraaH1Y+Jjieq/BpBnALyz/quln3QltfAs+goxC0WWIPP2OQD67XA2hKFGNL013verx0raxRi32dIaUX2/s4gWKDORbVJ7nNl8YGFBi8CpvWB9Ss+d1+dmw9s7d4t74OaGy9l8YFtKU8/a73FpI5XEtC2+cUoIebgBDYnchk8lqMNIDkSth2qU0nyLgV9ksY3bcwtPXlPdgK3ENMCSwxgagJrCkYhtpdOtMAm6RPKiunP6BAzNG0h7gI9bDccGeWAe4XO+02syMiknsiLfiu43pWcUEuYp0Nq13ayTCveDXeCYi2wUQT5wJqKQuyWw20drUjF1yJhYgXcLXeUn4/vKm3Es6aJQhz7MzpSMe5L6CdP3Ouj3vVIC2zteGof2A6IQpyXK0bR0Wnd5CGPXxRiEz5yJqIQ15uVKB9YN5n1oN51ttMH1uPDndWK7P2dWq4YO2diWWA9YxNbLbBhUYiBdPXhG2/EZX3Lqzy8UYgbxxH+jEkI6vfTjAtsc18IIrcoxG4DbEn7YZvhBDYn8tr6RrqbvALb2ExePrBxLbB5BV+IsnaYCr5Dupv6yr8t/QctsHYjSBYMz3a6YdcSLbB2wiBO+cIJbA64LQdJfWDdeK17gT6wIatcft8LqyXOlSBrcRys8IFNEIW45Vo0R/M1EYW4/n9dt/zKxnQU4rDyD5osRskQq1y9FqoEPrBJVjnjWsiz+HGHEWUhNbX101bLSBS5RSFOE3k0T8Tc6rzfu2syCnEmXfL4w7X4i6G5vfSTN3MU4jZXvF8U4qTkscsmTJaKVMItsD51ULRfeRRBuw/SyB3W79fzzM0CW+//fRY/jY2XJEDPYvizBvm1207W3Uot7a7PDkxiHqMTWBG5V0QeF5GPJEkT57qykcvAiytsXU83rrTGfl5JtsppSxTiIN+YxtZP+sCSDNj2PeFCLLCWPHsZCAriU1amdKrj44d047igDLjfI1pgzdNrKiMR+TMAPar6OhH5ioicpqq/j0oD4Oyo68rEzuGdGJ8cbzQmQ6ND2Dm8EwBweOJwZCPjtbj6reDuHtmNak+1cXz/6P6m83757RzeiYHeARwaP9Rynzyoy7Ln8J7G88dl7+G9eYiUmJGJkVDZh8eGfa2hIoLRidHG6tvk1GTiMgCAQ2OHjvqLQTA8Ptzw56nX366RXY309XLLEoV4eHz4qD6F+bpAcHjicMtzhemi+znCymPvkeb6j+0DiwqmMBW7rEfGRwAAE1MTLdccGDvQ+D02OZa4/kYmRiLfsfpz7R/d75t/vRxSW9RrdXBg7EAq/SuaiamJ3HxgJ7S1ztvF+NR4izzjU+NG5HHrbb3sJnUSQPaFEBFJ3ZYBTl/YlJ/HArt7ZDcA/3YsrS7vO7Kv6R5DR4baVu/jU+NNE6e6DEHvexB7Du9p/D40diiz/KMTo+FtuwgOjh0MvM/o5KiTzjNW8WtHbWB0chQDvQO+54L6sTDGJsf8x1qub+iKCI5MHDFeHsPjw065124/eGTQGXNOjRv1gfWr//HJ8cb5sGvd/eXQEeedt30BqTFOHtmJab3TEl/faLM8bVq97QWcMfiM8RlZRTXOnIE56Kv0FS1GKoxNYAGsBPBg7fdPAFwKwDsR9UtzXozrSsPV370aY1NjWDRjEYZGh/DxdR9vOr/k2CWh1/f1HFWkak+16f/pfdMxeGQQN/74Rv9rfZSwPtG95qFrGsd6pTf3CWxdlpsfvjl1Hu5Jerup9lSxdutarN26NjTdzL6ZqFYcOecMzAHgPPv6XesbafYe2Ysrv31lKjnqjWm1p4rv/P47jd91vbj+R9e3yl5JV27VShXf/f13j/4fUv7Vnip+vv3ngc8VJEO1p4rVz6/G6udXR8rTK72Y0InYetDf04+JqYnEZb378O7Qa/6w/w+p6m/B9AWh5+vvyJ0b7sSdG+4MTNff05/43sDR9/zujXfj7o13p8qjaM6cc6bxPPsqfRg8Mpj6nTTJMb3HoNpTxQv7XzAmj0DQW+ltLIAOHhlEj/RkHuBWK1W8cviVzHLO6JuBfaP7Gm1Y/f2+7kfXtdyvTl2X79p4F+7aeFfie07vmw4A+MTjn0gpdTqWzlra+N1Tcergnt/cg3t+c0+q/B587kE8+NyD0QkjOO+E8wLP9ff04/7N9+P+zfeH5uFul/t6+vDSgZeseKf8WLlope/xak8Vj25/NLHcK05c0XKs3p739/SjWqniiR1P5FIeJwyc0Hg3PrbuY43jZxx/hpH8qz1VPLD5ATyw+YHA80H09fT59pdpxyTtov5Mb3vobUbyEQh6pRf7Rvc1zl37/Wsz5Z0Xq69ZjWWzlxUtRirE1FYREbkXwH+o6kYReTOA81X19qg0AE6Lcd1NAG4CgMWLF1+wZcsWIzLnwUPPP4QpncIF8y/Ac0PP4eDYwabzZ8w+A8vnLg/N42d//Bn2Ht6LSxdeinnT5uGRbY9g6MgQVixYgV/t/hXGJsdarpk7MBdvXPTGluMHxw7i4a0PN60ELZyxECsWtDbAJhmfGseaLWtweOJwqusHegdwxSlXFLYy9MzeZ7B5cHNkulOOPQXL5yzHmq1rsHzOcpw661Q8P/Q8nt7zNCpSwfI5y7Fpz6bUvhCLZy7GhSdeiF/v/jVe3P8iBII3nfwmDPQOYM2WNS3WnCzltmH3Bryw/wUAwOz+2Vi1eFVg2s2Dm/HM3md8z/VWenH54ssbg8age4Qx0DuAs+eejU17N+GKxVegtxK91jZ0ZAiPbHskUVnP6p/VsBp76a304lXHvwqb9myKnZ+bZcctwznzzglN89j2x5qs6C3yVWfhssWXpV7BXrd9HXaO2GcVicuKBSuwcMZCo3nuHtmNx7Y/Vqh/0llzz8K2A9uw8uSV2DG8A7/Y+Qtjec+fNh+XLLwEwNH6P2nGSbh4wcWZ8t01vAvrXl6Xqdxm9c/CqbNOxYbdG3D+CedjyawlGBkfwdqta5vaMr92LK0uz+6fjZUnr8TarWubLNTt4LUnvhYnzzy58f+6l9elsspVe6qYc8wc7BjeYUSus+eejdNmn+Z77qldT2HLgfAxVo/04LLFl2FmdSYAYPuh7Xhyx5NGZMuDC+ZfgFOOPaXl+LODz+K3e3+bOL/XzHsNlh63tOnYnsN78OgfH8XS45ZioHcgdb8RxdJZS3HuvHOxZuuapvHlRSdehEUzF2XOP6z+e6QHqxavwrHVY33Pbzu4Db/c+cumY8dWj8Xliy+32grrN05OSn9PP6485crGJPbJHU9i+6HtmN43HaOTo5iYmjAlrlEuX3w5ZvXPKlqMQETkKVW90PecwQnsvwP4pqo+Udsq/CpV/UxUGgDzo65zc+GFF+r69euDThNCCCGEEEIIKTFhE1iT0UGegrP9FwDOBfBSzDRxriOEEEIIIYQQ0uWY9IF9CMCjInISgLcAeI+IfFpVPxKS5mI4wbm9xwghhBBCCCGEkCaMWWBV9QCcIE1PAFilqhs9k1e/NPv9jpmSiRBCCCGEEEJI52DMB7ZdiMgrAOyN4nSUWQA4GbeDWQD6AOyJSkhyp8zvxVx0ng6VuT7KSJgOsS7swea66MR2KAyb66KsZNEh1oc9FFkX7WqHTlHVeX4nSjeBLQsi8mVVvaloOYhTF3CiW/s6gpP2Ueb3QkTWd5oOlbk+ykiYDrEu7MHmuujEdigMm+uirGTRIdaHPRRZFza0QyaDOJFmflC0AKQB68IeWBd2wfqwB9aFPbAu7IEPiSk+AAAIO0lEQVR1YResD3vo6rqgBZZ0BTasFpFyQx0iWaEOkaxQh0hWqEMkKzboEC2wpFv4ctECkNJDHSJZoQ6RrFCHSFaoQyQrhesQLbCEEEIIIYQQQkoBLbCEEEIIIYQQQkoBJ7CEEEIIIYQQQkoBJ7CkIxER6jbJhIj0FC0DIaS7YV9GsiAiUrQMpPzY2A5ZJxAhaRGRy0XkVhGZo6pTRctDyoU4zBORrwKAqk4WLRMpHyJytogwwidJjYhcKSJ/JSID7MtIUkRklYhcDQDKQDckJbaPqTmBJR2BiHwOwN8CmA/gQyKysGCRSMmodfQVAG8RkXcCdq46Euv5OwCzixaClBMRuQPALQDOB/D3tWO0opFQ6joiIjcDuBXAFSLyARE5vVjJSJlw6dHnYfmYmoMz0ikMAngfgA8DWA6AHT6JjWuieiKAHQBuEJFpqjrFrcQkDiLSV/u5FcBVInKNiFwiIrNq59kmkTjshtOXfQDOJKSPVjQShoj04+ii2QCAr6vqh+Do0ltF5PjChCOlwaNHrwC4CRaPqTmBJaVERM4Skc/Ufk8DsB7AAVUdhTOZ5aojCcWtQy7GAdwJ4Ak4A0huJSaBuHVIVcdrh1cBGK79fgeAf6qd5ySEtODTlwHAXwN4D4DnALxXRFYWJB6xHBG5EcB/A/iciKwCMAGgUluUfQzAJICrChSRlACXHt0uIm8G8GsA+2weU3MCS8rKMgDXichZqjqiqv+rqhMiMh3A6ar6MNBkFSHES12HznT5dywAsERVPw3gWhF5QEQWFScisZyGDrmOPQVgj6p+H8DnASy0cfsVsYa6Dp2tqiMAvgbgSQBLANwO4DgAb671bYQ0EJF5AK4B8DcANgF4NYCXAJwHYA6A7QC2ADjJtThCSBMePfodgPNU9SeqOmnzmJoTWFIKRGSxiLxbRBbUDk0DsBrAba40fXBWHx8XkTNE5NMALm6/tMRGQnToo65kkwDGROQLAGbAmcz+sc2iEkuJqUMjcHSoAmARAFHV7W0WlVhKVF+mqrvhbON7WFVfAHAEwEJVHfbNkHQVLv05EU778qCqPg/g/wC8U1VXw9GZqwH0ANgP4A21xRFCAITq0U/h6E7dtWoCwDobx9ScwBLrEZGzAHwbwDkAbhORiwB8T1U/CGerzJ8DjS18pwP4BwD/CWC3qj5akNjEImLo0LtqSXsBXAtgk6qeCeBTteut8/8g7SWGDr27lvRHAOYCuA/AHXDcGwiJ0iFxtUO7AHxQRO4H8G9wtoKSLselP+fCGefMVNVv1k4PAnix9vsrAGbC2RL6VgDPtFlUYjERerQPwLMAUNuZdjqAf4SFY+reogUgJAgReQecgeDPADyrqreJyFUALoGzav0InC16nxSR79RettkAPgngS6o6WIzkxBYS6NCnROTbqrpGRN6oqgcBQFV/WPtL/8UuJWE79C1VXS8iTwG4FM5CyFBBohNLSNEO/UBEfgfH2vH+mlWWdCk++nNrTX8uEpEJVV0HYCEcaysA9AG4B84W4oMANhQgNrGMBHp0sJb+HAAnw9lhdJdtY2rhuIzYhoj0Ari39u9hOI2yAPgsgDEAfwLHb+hLqnpIRO4EMKiqHxWRHgbdISl1aK+qfqx2fcXG756R9pFVhwjJ0pcVIS+xiwT6cwcc/8ULARwDJ/rwrap6qN0yE/tIqUcDcPynb7dt4lqHW4iJjUwB+IOq/gWcLZyXwtmjv6zmB/QsgCqcIBeAszr0U4ARY0mDNDr0SP1iTl4JMuoQIcjQlxGCePozDY7VbD6A1wP4L1V9PyevxEUaPfqGqn7Y1skrwAkssZMKnIAEUNWXAfwWwMsA/lRElsF52VbBWUmCqg7WI6QRUoM6RLJCHSJZoQ6RLMTRnzfU0t6nqqfXop8T4qYj9Yg+sMQ6VHUCtZdNRE4GcKqqXiEi7wHwLwCG4Ph2cIWR+EIdIlmhDpGsUIdIFmLqzw4AR1R1a3GSEpvpVD3iBJZYSy2E9zicEN6vBnAGgB/Dedm2qequIuUj9kMdIlmhDpGsUIdIFmLoD4N8kUg6TY8YxIlYjYhcA+AhAD+B49vxtYJFIiWDOkSyQh0iWaEOkSxQf4gJOkmPOIElViMiqwCsAPAFVR0rWh5SPqhDJCvUIZIV6hDJAvWHmKCT9IgTWGI1IiL8BifJAnWIZIU6RLJCHSJZoP4QE3SSHnECSwghhBBCCCGkFPAzOoQQQgghhBBCSgEnsIQQQgghhBBCSgEnsIQQQkhBiMh9IrJBRNaLyPtC0q0UkSXtk4wQQgixE05gCSGEkGK5GcBVAD4uIucEpFkJYEm7BCKEEEJshRNYQgghpGBUdS+AHwJ4u4j8XEQeFZF/BQAR+SqAGwB8UUS+UTs2X0T+R0TWicgthQlOCCGEtBlOYAkhhBA72AtAAfwzgLcAeCsAqOqNAO4D8EFVva6W9hYAD6jq6+FMeue0X1xCCCGk/fQWLQAhhBBCAADHA9gGZwJ7CMDMkLRnAHidiNwAYDqAk+BMgAkhhJCOhhNYQgghpGBE5Dg4VtdfAPgsgI0AnnYlOQxgWi2tANgM4Huq+lMRuR7AYHslJoQQQopBVLVoGQghhJCuRETuA3AugFEAX4SzhfgjAF4BcAKAq1R1u4gsA/AVAFUA18Ox0N4L4DgALwK4QVUn2v4AhBBCSJvhBJYQQgghhBBCSClgECdCCCGEEEIIIaWAE1hCCCGEEEIIIaWAE1hCCCGEEEIIIaWAE1hCCCGEEEIIIaWAE1hCCCGEEEIIIaWAE1hCCCGEEEIIIaWAE1hCCCGEEEIIIaXg/wFtXuYi0YG0XQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "TradeAccount.plot(subplots=True, figsize=(16,8),title='成交量与均线策略交易账户')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2010-08-10    36.50\n",
       "2010-08-11    34.09\n",
       "2010-08-12    31.20\n",
       "2010-08-13    30.80\n",
       "2010-08-16    31.50\n",
       "              ...  \n",
       "2021-10-15    71.37\n",
       "2021-10-18    78.51\n",
       "2021-10-19    78.48\n",
       "2021-10-20    80.10\n",
       "2021-10-21    79.05\n",
       "Name: Close, Length: 2629, dtype: float64"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
